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Повышаем надёжность промышленного оборудования с помощью компьютерного зрения

Привет, Хабр! Меня зовут Павел Криницин. Я работаю на крупном металлургическом предприятии по производству и переработке алюминиевой продукции, где слежу за работой оборудования. В этой статье я расскажу, как мы исследовали способы раннего диагностирования повреждений конвейерных лент с применением компьютерного зрения. Эта статья будет полезна широкому кругу специалистов, занятых в различных областях промышленности, где применяют конвейерные транспортные системы. Описанные в статье подходы делают диагностику оборудования и поиск дефектов более точными, а планирование ремонтных работ — более эффективным.

Павел Криницин

Работает аналитиком на крупном металлургическом предприятии и применяет алгоритмы машинного обучения и цифрового зрения для оптимизации производственных процессов.

Но прежде чем мы перейдём к содержанию исследования, скажу пару слов о самом оборудовании и его роли в производственном процессе.

Транспортное оборудование занимает большую долю всех промышленных объектов на металлургических предприятиях. От работоспособности ленточных конвейеров зависит функционирование всего производственного процесса. Транспортёры обеспечивают непрерывность подачи сырья на очередной производственный участок для его дальнейшей обработки и перемещают готовую продукцию на следующие этапы производства или на отгрузку.

Устройством, которое перемещает грузы, могут быть металлические пластины — траки — или транспортёрная лента. Сфера их применения различна, у каждого есть свои преимущества, недостатки и технические особенности: сложность ремонта и монтажа, дороговизна запасных частей, ограничения по месту установки. Часто на производстве используют конвейеры с транспортёрной лентой, изготовленной на ПВХ- или резиновой основе. Их доля среди транспортёрного оборудования составляет более 90%.

Рисунок 1. Металлическая цепь транспортёра, собранная из отдельных стальных пластин — траков, — не боится воздействия посторонних твёрдых предметов, которые способны повредить мягкую транспортёрную ленту

Срок службы резиновой конвейерной ленты — в среднем один-два года, металлических траков — десятки лет. Учитывая высокую частоту отказов оборудования для транспортировки и переработки нефтяного кокса, повышение надёжности транспортёрной ленты — приоритетная и особо актуальная задача на производстве.

Рисунок 2. С точки зрения срока службы транспортёрной ленты чаще всего проблемы возникают у конвейерного оборудования

Вот основные проблемы, которые возникают при эксплуатации транспортёрной ленты конвейеров:

Износ ленты — со временем лента истирается о транспортируемые материалы и изнашивается, это приводит к образованию сквозного износа и деформациям.
Попадание посторонних предметов, которое механически повреждает ленту, создаёт на ней разрывы и сквозные дефекты.
Повреждение резинового слоя ленты экстремальными температурами от воздействия нефтяного кокса после прокалочной печи и холодильного барабана — на рабочей поверхности образуются трещины, их глубина и интенсивность постепенно увеличиваются, и лента рвётся.

Рисунок 3. Стадии развития дефекта транспортёрной ленты в результате повреждения рабочего слоя

Для защиты от высокой температуры применяется лента марки 2Т3, но и она не выдерживает длительного контакта с разогретым прокалённым коксом.

На срок службы транспортёрных лент могут влиять и другие факторы — например, фракционный и химический состав сырья: прокалённый и сырой нефтяной кокс, анодная масса. При перегоне сырого нефтяного кокса зимой на транспортёрную ленту воздействуют химические реагенты, которые уменьшают смерзание сырья. Чтобы снизить их вредное воздействие, используют маслостойкую транспортёрную ленту 2МС. Но постепенно её рабочая поверхность размягчается, теряет эластичные свойства и деформируется.

На срок службы транспортёрных лент влияют и разнообразные условия эксплуатации оборудования. Часть оборудования работает непрерывно, другая — по установленному графику. Различается и загруженность транспортёров сырьём: постоянная загруженность или периодически изменяющаяся в процессе работы, эксплуатация на предельной производительности или в щадящем режиме. Из-за этого установить нормативный срок эксплуатации для транспортёрных лент невозможно: он сильно варьируется для аналогичного оборудования даже в пределах одного цеха.

Специально обученный персонал сервисной службы выполняет регулярную классическую визуальную диагностику технического состояния лент в процессе эксплуатации. Но у этой процедуры тоже свои недостатки. Во время осмотра лента обычно находится под слоем материала, а при большом количестве оборудования невозможно одновременно остановить все транспортировочные системы. Проблема человеческого фактора тоже имеет большое значение: каждый пропущенный специалистом дефект может остановить конвейер и всю производственную линию, что приведёт к крайне негативным последствиям.

Именно поэтому так важно внедрять дифференцированный подход к обслуживанию оборудования, который учитывает степень влияния конкретного конвейера на производственный процесс в целом. Повреждения ленты могут быть разными: одни требуют немедленного устранения, другие позволяют планировать ремонт в прогнозируемые сроки. Поэтому перед нами встала задача разработать и внедрить автоматизированную систему диагностики ленты в процессе эксплуатации. Такая система значительно повысила бы точность и своевременность обнаружения дефектов, обеспечила бы более стабильную и безопасную работу производственных линий.

Чтобы справиться с характерными неисправностями ленты на нашем производстве: трещинами поверхности, деформациями и локальными разрывами или сквозными отверстиями, — мы изучили возможности самостоятельно реализовать алгоритмы компьютерного зрения, так как хотели оценивать состояние транспортного оборудования без привлечения ремонтного персонала, то есть в автоматическом режиме.

Компьютерное зрение — это технология, позволяющая компьютерам «видеть» и интерпретировать визуальные данные путём анализа изображений или видео. Она применяет методы обработки изображений, машинного обучения и искусственного интеллекта, чтобы выполнять задачи детекции и сегментации объектов, которые раньше могли выполнять только люди.

Так, с помощью видеокамер и алгоритмов анализа изображений мы отследили состояние транспортёрной ленты в режиме реального времени, а система автоматически выявила признаки износа — трещины, разрывы.

Классификатор состояний ленты на основе предварительно обученной модели нейросети ResNet-18

Мы обработали видеопоток с камеры со скоростью записи 250 к/сек и получили изображения транспортёрных лент с дефектами. Присвоили изображениям классы в соответствии с доминирующим дефектом на них. Например, если на ленте были разрывы и трещины, то изображению присваивался класс «Разрыв ленты» — как наиболее важный с точки зрения дальнейшей безотказной работы оборудования.

Чтобы повысить универсальность работы модели, выполнили аугментацию изображений на обучающей выборке:

transformed_dataset = []
transform = transforms.Compose([
    transforms.RandomHorizontalFlip(p=0.5),  # Случайное отражение по горизонтали с вероятностью 50%
    transforms.RandomRotation(degrees=(-15, 15)),  # Случайный поворот на угол от –15 до 15 градусов
    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1)  # Изменение цветовых характеристик
])

Исказили положение изображений и их цветовую гамму.

Рисунок 4. Изменяем положение изображений — камера может быть установлена под разными углами над конвейером — и моделируем различную освещённость объекта

Далее импортировали из пакета Torchvision библиотеки PyTorch предобученную модель ResNet-18, заморозили все слои модели и поменяли последний полносвязный слой на новый — с четырьмя выходными классами. Определили функции потерь и оптимизатора:

model = models.resnet18(pretrained=True)
for param in model.parameters():
    param.requires_grad = False
num_classes = 4  # Количество классов в нашем датасете
model.fc = nn.Linear(model.fc.in_features, num_classes)
# Определение функции потерь и оптимизатора
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

Потом пробно обучили модель-классификатор на 20 эпохах, получили значение ошибки Loss — 7%. Учитывая, что на обучающей выборке было всего 224 изображения, мы получили неплохие результаты.

Чтобы проверить работу модели, загрузили изображение из тренировочной выборки. А после обработки моделью получили заключение о состоянии ленты.

Рисунок 5. Результаты работы модели

Таким образом, мы выяснили, что благодаря классификатору можно выявлять наличие или отсутствие дефектов на ленте и определять, какая визуальная диагностика нужна: с привлечением специалистов сервисной организации или с помощью алгоритмов детекции и сегментации. Такой алгоритм работы прост в реализации, его можно применять для диагностирования большей части конвейерного оборудования и контроля его исправного состояния.

Детектор дефектов транспортёрной ленты с использованием Fast R-CNN из библиотеки PyTorch

Чтобы обучить модель-детектор, нужен был набор изображений с размеченными дефектами. Мы размечали их в программе CVAT. Классы распознаваемых дефектов — трещины, порывы ленты, состояние стыка ленты.

Рисунок 6. Результаты разметки дефектов транспортёрной ленты сохраняются в формате .xml

В программе каждому изображению присвоили свой ID-номер, название дефекта и его координаты. Для одного изображения могло быть несколько отметок о дефектах.

Затем с помощью методов Python создали датафрейм на основе XML-разметки. Для этого определили функцию def parse_annotation: она анализировала XML-файл с аннотациями для изображений. Функция parse_annotation преобразовывала XML-данные в список словарей, каждый из которых содержал подробную информацию об одном объекте изображения: имя файла, размер изображения, метку класса и координаты точек полигональной линии.

def parse_annotation(xml_file):
    tree = ET.parse(xml_file)
    root = tree.getroot()

    objects = []
    for image in root.findall('image'):
        filename = image.get('name')
        width = int(image.get('width'))
        height = int(image.get('height'))

        for polyline in image.findall('polyline'):
            label = polyline.get('label')
            points = polyline.get('points')

            objects.append({
                'name': filename,
                'width': width,
                'height': height,
                'class': label,
                'points': points
            })
    return objects

Проверили разметку изображения на основании созданного датафрейма.

Рисунок 7. Три класса разметки на изображении участка транспортёрной ленты

Далее создали класс нашего датасета (class CustomDataset(Dataset)), — таким образом мы подготовили данные в удобном формате для загрузки батчами с помощью DataLoader из PyTorch:

class CustomDataset(Dataset):
    def __init__(self, root, df, transforms=None):
        self.root = root
        self.transforms = transforms
        self.dataframe = df
        self.imgs = list(self.dataframe['filename'].unique())
    def __getitem__(self, idx):
        img_name = self.imgs[idx]
        img_path = os.path.join(self.root, img_name)
        img = Image.open(img_path).convert("RGB")
        current_annotations = self.dataframe[self.dataframe['filename'] == img_name]
        num_objs = len(current_annotations)
        boxes = []
        labels = []
        for _, row in current_annotations.iterrows():
            boxes.append([row["xmin"], row["ymin"], row["xmax"], row["ymax"]])
            labels.append(row["class"])

        boxes = torch.as_tensor(boxes, dtype=torch.float32)
        labels = torch.as_tensor(labels, dtype=torch.int64)
        image_id = torch.tensor([idx])
        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
        iscrowd = torch.zeros((num_objs,), dtype=torch.int64)
        target = {"boxes": boxes, "labels": labels, "image_id": image_id, "area": area, "iscrowd": iscrowd}
        if self.transforms:
            img, target = self.transforms(img, target)
        return img, target
    def __len__(self):
        return len(self.imgs)

И создали функцию обучения модели-детектора. Обучили модель:

def train_model(num_classes, num_epochs, batch_size, device):
    dataset = CustomDataset(img_path, df, transforms=get_transform(train=True))
    data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=0, collate_fn=collate_fn)
    class_weights = calculate_class_weights(df)
    criterion = torch.nn.CrossEntropyLoss(weight=class_weights)
    weights = FasterRCNN_ResNet50_FPN_Weights.COCO_V1
    model = fasterrcnn_resnet50_fpn(weights=weights)
    in_features = model.roi_heads.box_predictor.cls_score.in_features
    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
    model = model.to(device)
    params = [p for p in model.parameters() if p.requires_grad]
    optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005)
    lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)

    for epoch in range(num_epochs):
        model.train()
        epoch_loss = 0
        progress_bar = tqdm(data_loader, desc=f'Epoch {epoch + 1}/{num_epochs}', leave=False)
        for images, targets in progress_bar:
            images = list(image.to(device) for image in images)
            targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
            loss_dict = model(images, targets)
            losses = sum(loss for loss in loss_dict.values())
            epoch_loss += losses.item()
            optimizer.zero_grad()
            losses.backward()
            optimizer.step()
            progress_bar.set_postfix(loss=epoch_loss / (progress_bar.n + 1))
        lr_scheduler.step()
        print(f"Epoch {epoch + 1} completed with loss {epoch_loss / len(data_loader)}")
    print("Training completed.")
    return model

Обученная модель распознавала объекты на изображениях из тренировочной выборки.

Рисунок 8. Результаты работы модели-детектора

Модель-детектор улучшила диагностику транспортёрной ленты: позволила не только выявлять неисправности, но и оценивать их масштаб. Например, теперь можно было рассчитать площадь выявленных повреждений. Эта информация особенно важна для определения дальнейшей стратегии в ремонте транспортёрного оборудования.

Применение такой модели подходит для оборудования, которое установлено в местах с затруднённым для сервисного обслуживания расположением — стеснённые условия, недостаточная высота потолков, — и позволяет полностью исключить диагностику специалистами сервисной службы.

Сегментация изображений участков транспортёрной ленты с использованием Mask R-CNN из библиотеки PyTorch

С помощью сегментации изображений обычно выделяют объекты и их границы на изображениях. Так мы определили функцию создания сегментационной маски объектов и их последующую визуализацию:

def create_segmentation_mask(height, width, points):
    mask = np.zeros((height, width), dtype=np.uint8)
    points = points.split(';')
    points = [list(map(float, p.split(','))) for p in points]
    points = np.array(points, dtype=np.int32)
    cv2.fillPoly(mask, [points], 1)
    return mask
def show_images_with_masks(images, masks, num_images=3):
    combined = list(zip(images, masks))
    random.shuffle(combined)
    images, masks = zip(*combined)

    fig, axes = plt.subplots(num_images, 2, figsize=(10, 15))
    for i in range(num_images):
        axes[i, 0].imshow(images[i])
        axes[i, 0].set_title(f'Image {i+1}')
        axes[i, 1].imshow(masks[i], cmap='gray')
        axes[i, 1].set_title(f'Mask {i+1}')
        
    plt.tight_layout()
    plt.show()

Рисунок 9. Маски объектов на изображении дефектного участка ленты

Создали модель сегментации изображений для обработки датасета, обучили её:

# Загрузка модели
weights = MaskRCNN_ResNet50_FPN_Weights.COCO_V1
model = maskrcnn_resnet50_fpn(weights=weights)
# Количество классов (например, 1 класс + фон)
num_classes = 3
# Замена box_predictor
model.roi_heads.box_predictor = torchvision.models.detection.faster_rcnn.FastRCNNPredictor(
    model.roi_heads.box_predictor.cls_score.in_features, num_classes)
# Замена mask_predictor
model.roi_heads.mask_predictor = torchvision.models.detection.mask_rcnn.MaskRCNNPredictor(
    model.roi_heads.mask_predictor.conv5_mask.in_channels,
    model.roi_heads.mask_predictor.conv5_mask.out_channels,
    num_classes
)
# Проверка модели
print(model)

Чтобы оценить качество обучения модели, визуализировали предсказания модели на новых изображениях.

Рисунок 10. Сегментированные дефекты на транспортёрной ленте

Сегментация дефектов на ленте расширяет возможности диагностики, позволяя более точно рассчитать площадь повреждения ленты и предсказать остаточный срок эксплуатации. Характер расположения объектов на изображении указывает на причины возникновения дефектов, помогает разработать корректирующие мероприятия, чтобы исключить или ослабить влияние этих дефектов.

Конвейерное оборудование обычно устанавливают и эксплуатируют в местах с повышенной запылённостью и влажностью, поэтому камеры должны быть с достаточной степенью пылевлагозащиты. Мы использовали видеокамеры со степенью защиты IP64.

После обучения алгоритма размеченный датасет с изображениями участков транспортёрных лент показывает хорошие результаты и отлично выявляет дефекты на ленте. Этого достаточно, чтобы детально определить техническое состояние ленты и решить, нужно ли проводить её визуальный контроль с привлечением специалистов по сервисному обслуживанию.

Алгоритмы, обученные с помощью координат вершин ограничивающих прямоугольников, предоставляют гораздо больше информации о самом дефекте: его приблизительном размере, месте расположения, характере повреждений. По этой информации можно предсказать срок службы ленты, примерно оценить площадь её повреждения и зафиксировать воздействие на неё высокой температуры или химических реагентов.

Сегментация объектов на ленте даёт более качественное представление о площади повреждения и точно фиксирует повреждённые области. Применять сегментацию целесообразно для особо ответственного оборудования, так как его безаварийная работа обеспечивает работоспособность всей технологической линии производства.

В дальнейшем мы будем оптимизировать систему обслуживания оборудования по категориям важности для производственного процесса на основе полученной информации о работе моделей и интегрировать этот алгоритм в систему управления предприятием — MES.

Чтобы расти, нужно выйти из привычной зоны и сделать шаг к переменам. Можно изучить новое, начав с бесплатных занятий:

Или открыть перспективы с профессиональным обучением и переподготовкой:

Системный аналитик PRO;
Data Scientist: расширенный курс;
Машинное обучение;
Онлайн-бакалавриат «Аналитика и Data Science»;
Deep Learning.

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4 июл. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
24.06.24 04:31 tashandiarisha

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26.06.24 18:46 Jacobethannn098

LEGAL RECOUP FOR CRYPTO THEFT BY ADRIAN LAMO HACKER
26.06.24 18:46 Jacobethannn098

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04.07.24 04:49 ZionNaomi

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13.07.24 21:13 michaelharrell825

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17.07.24 02:26 thompsonrickey

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18.07.24 20:13 austinagastya

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27.08.24 12:50 James889900

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27.08.24 13:06 James889900

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02.09.24 20:24 [email protected]

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06.09.24 01:35 Celinagarcia

HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.
06.09.24 01:44 Celinagarcia

HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.
16.09.24 00:10 marcusaustin

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16.09.24 00:11 marcusaustin

Bitcoin Recovery Services: Restoring Lost Cryptocurrency If you've lost access to your cryptocurrency and unable to make a withdrawal, I highly recommend iBolt Cyber Hacker Bitcoin Recovery Services. Their team is skilled, professional, and efficient in recovering lost Bitcoin. They provide clear communication, maintain high security standards, and work quickly to resolve issues. Facing the stress of lost cryptocurrency, iBolt Cyber Hacker is a trusted service that will help you regain access to your funds securely and reliably. Highly recommended! Email: S u p p o r t @ ibolt cyber hack . com Cont/Whtp + 3. .9 .3. .5..0. .9. 2. 9. .0 .3. 1 .8. Website: h t t p s : / / ibolt cyber hack . com /
23.09.24 18:56 matthewshimself

At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.
01.10.24 14:54 Sinewclaudia

I lost about $876k few months ago trading on a fake binary option investment websites. I didn't knew they were fake until I tried to withdraw. Immediately, I realized these guys were fake. I contacted Sinew Claudia world recovery, my friend who has such experience before and was able to recover them, recommended me to contact them. I'm a living testimony of a successful recovery now. You can contact the legitimate recovery company below for help and assistance. [email protected] [email protected] WhatsApp: 6262645164
02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
18.10.24 09:34 freidatollerud

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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886 Email digital tech guard . com Telegram: digital tech guard recovery . com website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!
21.11.24 08:02 Emily Hunter

If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.
22.11.24 04:41 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
22.11.24 15:26 cliftonhandyman

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22.11.24 23:43 teresaborja

all thanks to Tech Cyber Force Recovery expert assistance. As a novice in cryptocurrency, I had been carefully accumulating a modest amount of Bitcoin, meticulously safeguarding my digital wallet and private keys. However, as the adage goes, the best-laid plans can often go awry, and that's precisely what happened to me. Due to a series of technical mishaps and human errors, I found myself locked out of my Bitcoin wallet, unable to access the fruits of my digital labors. Panic set in as I frantically searched for a solution, scouring the internet for any glimmer of hope. That's when I stumbled upon the Tech Cyber Force Recovery team, a group of seasoned cryptocurrency specialists who had built a reputation for their ability to recover lost or inaccessible digital assets. Skeptical at first, I reached out, desperate for a miracle. To my utter amazement, the Tech Cyber Force Recovery experts quickly assessed my situation and devised a meticulous plan of attack. Through their deep technical knowledge, unwavering determination, and a keen eye for detail, they were able to navigate the complex labyrinth of blockchain technology, ultimately recovering my entire Bitcoin portfolio. What had once seemed like a hopeless endeavor was now a reality, and I found myself once again in possession of my digital wealth, all thanks to the incredible efforts of the Tech Cyber Force Recovery team. This experience has not only restored my faith in the cryptocurrency ecosystem. Still, it has also instilled in me a profound appreciation for the critical role that expert recovery services can play in safeguarding one's digital assets. ENAIL < Tech cybers force recovery @ cyber services. com > WEBSITE < ht tps : // tech cyber force recovery. info > TEXT < +1. 561. 726. 3697 >
24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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25.11.24 02:20 briankennedy

After countless hours of research and desperate attempts to find a solution, I stumbled upon FASTFUND RECOVERY. It was like finding an oasis in the middle of a desert. Their website promised to help victims of scams reclaim what was rightfully theirs, and I instantly knew I had to give them a shot. Before diving headfirst into the recovery process, I wanted to make sure that FASTFUND RECOVERY was the real deal. So, I did my due diligence and looked into their expertise and reputation. To my relief, I found that they had an impeccable track record, successfully assisting countless individuals in recovering their lost funds. Their team consisted of experts in cybersecurity and financial fraud, armed with the knowledge and tools needed to tackle even the most intricate scams. With their reputation preceding them, I felt a renewed sense of hope. FASTFUND RECOVERY successfully came to my aid and got back the amount I lost to these scammers and for this, I am sending this article for clarification. The info of FASTFUND RECOVERY is email: Fastfundrecovery8 (@)Gmail (.) com. Web fastfundrecovery(.)com. (W/A 1 807/500/7554)
26.11.24 21:59 [email protected]

In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com
26.11.24 23:12 rickrobinson8

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26.11.24 23:12 rickrobinson8

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00:39 [email protected]

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09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.