Diagnosis of Bearing Faults in Electrical Machines Using Long Short-Term Memory (LSTM)

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Rolling element bearings are very important components in electrical machines. Almost 50% of the faults that occur in the electrical machines occur in the bearings. This makes bearings as one of the most critical components in electrical machinery. Bearing fault diagnosis has drawn the attention of many researchers. Generally, vibration signals from the machine’s accelerometer are used for the diagnosis of bearing faults. In literature, application of Deep Learning algorithms on these vibration signals has resulted in the fault detection accuracy that is close to 100%. Although, fault detection using vibration signals from the machine is ideal but measurement of vibration signals requires an additional sensor, which is absent in many machines, especially low voltage machines as it significantly adds to its cost. Alternatively, bearing fault diagnosis with the help of the stator current or Motor Current Signal (MCS) is also gaining popularity. This paper uses MCS for the diagnosis of bearing inner raceway and outer raceway fault. Diagnosis using MCS is difficult as the fault signatures are buried beneath the noise in the current signal. Hence, signal-processing techniques are employed for the extraction of the fault features. The paper uses the Paderborn University damaged bearing dataset, which contains stator current data from healthy, real damaged inner raceway, and real damaged outer raceway bearings with different fault severity. Fault features are extracted from MCS by first filtering out the redundant frequencies from the signal and then extracting eight features from the filtered signal, which include three features from time domain and five features from time–frequency domain by using the Wavelet Packet Decomposition (WPD). After the extraction of these eight features, the well-known Deep Learning algorithm Long Short-Term Memory (LSTM) is used for bearing fault classification. The Deep Learning LSTM algorithm is mostly used in speech recognition due to its time coherence, but in this paper, the ability of LSTM is also demonstrated with the fault classification accuracy of 97%. A comparison of the proposed algorithm is done with the traditional Machine Learning techniques, and it is shown that the proposed methodology outperforms all the traditional algorithms which are used for the classification of bearing faults using MCS. The method developed is independent of the speed and the loading conditions.