Deep Learning Based Spectrum Compression Algorithm for Rotating Machinery Condition Monitoring

Abstract

In the new data intensive world, predictive maintenance has become a central issue for the modern industrial plants. Monitoring of electric machinery is one of the most important challenges in predictive maintenance. Adaptive manufacturing processes/plants may be possible through the monitored conditions. In this respect, several attempts have been made to utilize deep learning algorithms for rotating machinery fault detection and diagnosis. Among them, deep autoencoders are very popular, because of their denoising effect. They are also implemented in electric machinery fault diagnostics in order to obtain lower order representation of signals. However, none of these efforts regard the autoencoders as compression units. Bearing in mind that spectra of vibration and current signals that are collected from electric machinery are critical instruments for detection and diagnosis of their faults, we propose that deep stacked autoencoder can be utilized as spectrum compression units. The performance of the proposed strategy are assessed using a bearing data set in three ways: (1)Rule-based classifiers are implemented on raw and compressed-decompressed spectrum and their performance are compared. (2) It is shown that the several machine learning classifiers such as support vector machines, artificial neural networks and k-nearest neighbour classifiers on compressed-decompressed spectrum achieves the performance of them on raw data. (3) A multi-layer perceptron (MLP) classifier is implemented on the low dimensional representation and it is demonstrated that the strategy of employing the same autoencoder as pretraining of feature extraction module cannot outperform the performance of this MLP classifier.

An Efficient Siamese Network and Transfer Learning-Based Predictive Maintenance System for More Sustainable Manufacturing

“…Deep-learning-based algorithms analyze complex and multichannel time series to predict maintenance time or detect a machine failure. Recurrent neural network (RNN) [120] or its improved versions [121][122][123], restricted Boltzmann machine (RBM) [124,125], autoencoder (AE) [126,127] and convolutional neural network (CNN) [128][129][130]-based deep neural networks (DNN) are the most commonly used techniques to monitor the current condition of a system or machine. Several studies have recently shown that DNN-based structures have several advantages over the above-mentioned conventional methods, such as SVM, DT, etc.…”

Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review

“…Equipment Fault Parameters Method [40] Motor Bearing Current signal CNN [41] CNC machine Condition Vibrations ANN [42] Motor Operations Current and voltage signal ANN/MLP [43] Pump Condition Multi variables AE [44] CNC machine Mechanical Vibrations signal SAE [45] Motor Operations Stator currents ANN [46] Motor Bearing Vibrations signal LSTM [47] Rotating machinery Bearing Vibrations signal AE+ MLP [48] Rotating machinery Bearing Vibrations signal LSTM [49] Cooling radiator Condition Thermal image CNN [50] Rotating machinery Degradation image Infrared image streams (CNN+LSTM) (LSTM+AE) [51] Compressor Condition Multi variables RNN-LSTM [52] Elevator system Movement Acceleration data AE [53] Motor Condition Current signal EWT-CNN [54] Autoclave sterilizer Pump NTC thermistors LSTM [55] Worm gearboxes Operations Multi variables CNN [56] Rotating machinery Rotor, bearing Vibration signals CNN [57] Railcar factories Wheel bearing Temperature variation ANN [58] Rotating machinery Bearing Accelerometers CNN [59] Motor Bearing Current signal ANN [60] Conveyors system Motor Multi variables CNN [61] Motor Bearing Accelerometer LSTM+RNN [62] Motor Rotor bar Torque control ANN [63] Motor Stator winding stator currents ANN [64] Motor Condition Vibrations signal ANN [65] Rotating machinery Bearing Rotation speed, load levels CNN [66] Motor Stator winding Multi variables MLP+LSTM+CNN [67] Motor Operations Current signal ANN [68] Motor+rotating equipment Bearing Vibrations signal CNN+DNN [69] Motor Bearing Microphone, accelerometer DCNN+CNN-LSTM+LSTM…”

A Multilabel Approach for Fault Detection and Classification of Transmission Lines using Binary Relevance

“…The growing usage of ML approaches in industrial uses results from adaptive modernization [9]- [10]. To accommodate many fault occurrences or dynamically changing load levels in the case of imperfect or noise assessments, the most recent fault detection and classification systems have increased their requirement for artificial intelligent methods [11]. A typical method for making diagnoses and forecasts is current signature analysis, which involves looking at the output current of the transmission line when functioning steadily [12]- [13].…”