This demo shows how to prepare, model, and deploy a deep learning LSTM based classification algorithm to identify the condition or output of a mechanical air compressor.

Fault Detection and Classification techniques using Deep Learning and Machine Learning based architectures to detect the UAV maloperation in Accelerometer and Gyroscope sensors.

Deep learning system for Nuclear Power Plant fault detection using CNN-LSTM neural networks with Self Aquila Optimization (SIAO). Achieves 100% accuracy on NPP sensor data for real-time fault classification including SGTR, PZRV, and power transient scenarios.

Predictive Maintenance of Induction Motors using Deep Learning Anomaly Detection using an Autoencoder Neural Network and Fault Classification using a Convolutional Neural Network

A deep learning algorithm using Tensorflow that achieved a 99% accuracy in fault detection and 97% accuracy in fault classification.

Faulty Solar Panel Detection Detects and classifies solar panel faults using deep learning. Classification: VGG16 ,MobileNetV2 with transfer learning. Detection: YOLO for fault localization.

A deep learning pipeline for bearing fault diagnosis using the Paderborn dataset. Combines a CNN-based Autoencoder for anomaly detection and a CNN classifier for multi-class fault classification (Healthy, Outer Race, Inner Race and ball fault). Uses envelope FFT features from vibration signals and is designed for industrial maintenance

Deep learning pipeline for automatic fault detection in solar PV systems using MobileNet for image classification and YOLOv8 for object detection. The project processes aerial and close-up images to identify and localize defects like dust, snow, and electrical damage.

These models were built as part of my undergraduate final year research project work. I built three deep learning models on 3 pretrained models: VGG19, ResNet152 and InceptionV3 using transfer learninig, for the detection and classification of three-phase induction motor stator faults. After retraining the various bases on processed fault signals

Undergraduate Thesis | Title: IoT-Based Fault Detection and Monitoring System in Solar Power Plant Using Machine Learning and Digital Twin Technology

Solar panel fault detection using U-Net-based deep learning image classification

A deep learning solution for automatic fault detection and segmentation in solar panels. Performs segmentation with ResNet50-based multi-class fault classification using EfficientNet-B0.

AI-powered 2D seismic fault classification using an artificial neural network (ANN). This project applies deep learning to geophysical data for automated fault detection, showcasing practical use of AI in seismic interpretation.

Overview This project implements an intelligent motor fault detection system using vibration or sensor signal data stored in CSV files. It combines machine-learning classification and deep-learning–based anomaly detection to identify multiple motor fault conditions and compute a quantitative health score.

Solar Panel AI – Deep learning project for automatic solar panel fault classification and defect detection using YOLO and related models, with training, dataset conversion, and analysis tools.

Power Grid Fault Detection and Classification using Machine Learning and a Hybrid CNN–LSTM Deep Learning Model. The goal is to: Build ML baseline models (Logistic Regression, SVM, Random Forest) using scikit-learn, and Build a CNN–LSTM model from scratch in TensorFlow/Keras for fault classification.

Developed an advanced deep learning system using ResNet-50 to detect faults in solar panels through image classification. The system automatically raises alerts for engineers upon fault detection, minimizing downtime and ensuring optimal power output.

An end-to-end automated railway fault detection system using drone-captured video, deep learning (TinyViT), Azure Blob Storage, and a full-stack Node.js web application. The system enables hands-off inspection with real-time fault classification, visualization, and reporting.

Automatic Machine Fault Detection system using Deep Learning on Acoustic Data. Identifies four fault types: Arcing, Corona, Looseness, and Tracking. Uses Mel Spectrogram images from audio data and a CNN model for classification. Achieves high accuracy with clear performance visualization via Confusion Matrix.

Early detection of cracks in building walls it is quite important as these are early indicators for the ageing, decaying or any internal structural fault. This project aims to develop an automatic inspection system based on deep learning model and image processing to identify cracks. Transfer learning models of convolutional neural networks (CNNs) are used to learn the intrinsic features of cracks using the images of the surfaces, which help them for the automatic classification into cracked/un-cracked classes.

The hands on project on Scene Classification and GradCam Visualization is divided into following tasks: Task #1: Project Overview/Understand the problem statement and business case Task #2: Import Libraries/datasets and perform preliminary data processing Task #3: Perform Image Visualization Task #4: Perform Image Augmentation Task #5: Understand the theory and intuition behind Convolutional Neural Networks (CNNs) and Residual blocks. Task #6: Build ResNet-18 Model Task #7: Compile and train deep learning model Task #8: Assess the Performance of the Trained Model Task #9: Visualizing Activation Maps through Grad-CAM Task #10: Save the model for deployment Task #11: Serve the model using Tensorflow Serving I did this project from Coursera Project Network. the link of the project is given below. https://www.coursera.org/learn/scene-classification-gradcam/home/welcome The project is designed by Ryan Ahmed. Ryan is a professor who is passionate about education and technology. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster University in Canada, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with a focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business.