Intelligent Fault Diagnosis of Rotating Machinery Using Deep Learning Algorithms: A Comparative Analysis of MLP, CNN, RNN, and LSTM

Health management in industrial systems is crucial for maintenance management, and it plays an important role in productivity, fault diagnosis, safety, efficiency, and economy in manufacturing industries. Early detection of faults in machinery may increase the effectiveness of maintenance actions and will avoid unwanted consequences in process operations and maintenance. Existing fault diagnosis methods have limitations such as insufficient accuracy, slow detection rate, and handling large and complex data sets. In this digital age, Industry 4.0 techniques have been applied across all fields, including the condition monitoring of machines. This research addresses the gaps in traditional fault diagnosis by using deep learning, a modern AI technique effective for diagnosing faults in various machines. In this research work, vibration signals are collected using the National Instruments- Data Acquisition (NI-DAQ) system, accelerometer, and LabVIEW software. These signals are processed using a series of steps, including sampling strategy, shuffling, standardization, and reshaping data augmentation. Deep learning algorithms Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) are tested for fault diagnosis using vibration datasets collected from Spectra Quest Machinery Fault Simulator (SQMFS). The result shows that MLP accuracy in the fault prediction is 0.9, CNN reached 0.95, and RNN and LSTM with 0.57 and 0.45, respectively. The high performance of CNN is due to its ability to effectively capture spatial patterns in vibration data, which is crucial for fault diagnosis in rotating machinery, followed by MLP due to its faster convergence during training. When the data is scaled, MLP performs better than CNN, demonstrating its adaptability to increased data complexity and volume. RNN and LSTM resulted in lower accuracy due to the need for larger datasets and temporal patterns in the vibration data, which they are designed to handle. This study shows that CNN has given better results than other deep learning algorithms, such as MLP, RNN, and LSTM, in fault diagnosis of rotating machinery. Future research could explore applying these techniques to different types of machinery and fault conditions.