Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features

H.O.A Ahmed,M.L.D Wong,A.K Nandi

doi:10.1016/j.ymssp.2017.06.027

Abstract

Abstract Condition classification of rolling element bearings in rotating machines is important to prevent the breakdown of industrial machinery. A considerable amount of literature has been published on bearing faults classification. These studies aim to determine automatically the current status of a roller element bearing. Of these studies, methods based on compressed sensing (CS) have received some attention recently due to their ability to allow one to sample below the Nyquist sampling rate. This technology has many possible uses in machine condition monitoring and has been investigated as a possible approach for fault detection and classification in the compressed domain, i.e., without reconstructing the original signal. However, previous CS based methods have been found to be too weak for highly compressed data. The present paper explores computationally, for the first time, the effects of sparse autoencoder based over-complete sparse representations on the classification performance of highly compressed measurements of bearing vibration signals. For this study, the CS method was used to produce highly compressed measurements of the original bearing dataset. Then, an effective deep neural network (DNN) with unsupervised feature learning algorithm based on sparse autoencoder is used for learning over-complete sparse representations of these compressed datasets. Finally, the fault classification is achieved using two stages, namely, pre-training classification based on stacked autoencoder and softmax regression layer form the deep net stage (the first stage), and re-training classification based on backpropagation (BP) algorithm forms the fine-tuning stage (the second stage). The experimental results show that the proposed method is able to achieve high levels of accuracy even with extremely compressed measurements compared with the existing techniques.

Highlights

Rolling element bearings are among the most fundamental elements in rotating machinery and their failures are accountable for more substantial failures in the machine
The classification in the deep net stage achieved good results for larger numbers of measurements, i.e., for values of m equal to 512, 256 and 128, and high accuracy was achieved by the two hidden layers deep neural network (DNN) using only 64 samples of our signal
Most of the classification accuracies for the two, three and four hidden layers DNNs using finetuning stage are 99% or above and some are 100% for even less than 1% compressed measurements of the original vibration signal, i.e., when a = 0.006

Summary

Introduction

Rolling element bearings are among the most fundamental elements in rotating machinery and their failures are accountable for more substantial failures in the machine. Several time–frequency analysis techniques have been developed and applied to machinery fault diagnosis, e.g., Wavelet Transform (WT), Short Time Fourier Transform (STFT) adaptive parametric time-frequency analysis based on atomic decomposition, and non-parametric time-frequency analysis, including, HilbertHuang Transform (HHT), local mean decomposition, energy separation and empirical mode decomposition [8,9,10,11,12,13,14,15]. Spectral Kurtosis (SK) has been used effectively in the vibration-based condition monitoring of rotating machines. In this method, signal is first decomposed into the time-frequency domain where the kurtosis values are defined for each frequency group. An alternative framework to strictly stationary vibration signal processing methods, cyclostationary analysis has been used for analysing vibration signals [18,19]

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