End-to-End CNN+LSTM Deep Learning Approach for Bearing Fault Diagnosis

TL;DR

This paper introduces an end-to-end deep learning approach combining CNN and LSTM for bearing fault diagnosis, achieving state-of-the-art accuracy without data manipulation, validated on benchmark datasets.

Contribution

The work presents a novel CNN+LSTM model that directly uses raw accelerometer data for fault detection, eliminating the need for feature engineering.

Findings

Achieves highest accuracy in literature for bearing fault detection.

Validates effectiveness on two benchmark vibration datasets.

Outperforms existing intelligent fault diagnosis methods.

Abstract

Fault diagnostics and prognostics are important topics both in practice and research. There is an intense pressure on industrial plants to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting and recovering potential faults in its early stages. Intelligent fault diagnosis is a promising tool due to its ability to rapidly and efficiently processing collected signals and providing accurate diagnosis results. Although many studies have developed machine leaning (M.L) and deep learning (D.L) algorithms for detecting the bearing fault, the results have generally been limited to relatively small train and test datasets and the input data has been manipulated (selective features used) to reach high accuracy. In this work, the raw data, collected from accelerometers (time-domain features) are taken as the input of a novel temporal sequence prediction algorithm to present an end-to-end method for fault detection. We use equivalent temporal sequences as the input of a novel Convolutional Long-Short-Term-Memory Recurrent Neural Network (CRNN) to detect the bearing fault with the highest accuracy in the shortest possible time. The method can reach the highest accuracy in the literature, to the best knowledge of the authors of the present paper, voiding any sort of pre-processing or manipulation of the input data. Effectiveness and feasibility of the fault diagnosis method are validated by applying it to two commonly used benchmark real vibration datasets and comparing the result with the other intelligent fault diagnosis methods.