Abstract
Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.
Highlights
Abnormal operations induced by gear or bearing failures should be detected as early as possible to avoid serious and even fatal accidents
Throughout the previous researches, we find that ANNs are one of the most commonly used classifiers in intelligent fault diagnosis, among which back propagation neural network (BPNN) is the representative one based on supervised learning [19]
This paper proposes a novel Artificial intelligence (AI) method based on a deep belief network (DBN) to achieve unsupervised feature learning could not be avoided when the Fourier spectral analysis is applied to nonstationary data [44]
Summary
Abnormal operations induced by gear or bearing failures should be detected as early as possible to avoid serious and even fatal accidents. According to the characteristics of the data, it is feasible to detect abnormalities in vibration signals and make decisions about the health conditions of gear or bearing by employing appropriate data analysis algorithms such as empirical mode decomposition (EMD) [7], spectral kurtosis [8], wavelet analysis [9], and time synchronous averaging [10]. Most of these methods depend on careful observation and recognition of the corresponding features of the vibration signals to identify the faults, which require a great deal of expertise to apply them successfully. Simpler approaches are needed which allow relatively unskilled operators to make reliable and rapid
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