Applications of machine learning to machine fault diagnosis: A review and roadmap

A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings

In recent years, deep learning has become an emerging research orientation in the field of intelligent monitoring and fault diagnosis for industry equipment. Generally, the success of supervised deep models is largely attributed to a mass of typically labeled data, while it is often limited in real diagnosis tasks. In addition, the diagnostic model trained with data from limited conditions may generalize poorly for conditions not observed during training. To tackle these challenges, adversarial learning is introduced as a regularization into the convolutional neural network (CNN), and a novel deep adversarial convolutional neural network (DACNN) is accordingly proposed in this paper. By adding an additional discriminative classifier, an adversarial learning framework can be developed to train the convolutional blocks with the split data subsets, leading to a minimax two-player game. This process contributes to making the feature representation robust, boosting the generalization ability of the trained model as well as avoiding overfitting with a small size of labeled samples. The comparison studies with respect to conventional deep models on two fault datasets demonstrate the applicability and superiority of proposed method.

A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults

A comprehensive review on convolutional neural network in machine fault diagnosis

Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine

Vibration signals of faulty rolling element bearings usually exhibit non-linear and non-stationary characteristics caused by the complex working environment. It is difficult to develop a robust method to detect faults in bearings based on signal processing techniques. In this paper, convolutional neural network -based hidden Markov models (CNN HMMs) are presented to classify multi-faults in mechanical systems. In CNN HMMs, a CNN model is first employed to learn data features automatically from raw vibration signals. By utilizing the t-distributed stochastic neighbor embedding (t-SNE) technique, feature visualization is constructed to manifest the powerful learning ability of CNN. Then, HMMs are employed as a strong stability tool to classify faults. Both the benchmark data and experimental data are applied to the CNN HMMs. Classification results confirm the superior performance of the present combination model by comparing with CNN model alone, support vector machine (SVM) and back propagation (BP) neural network. It is shown that the average classification accuracy ratios are 98.125% and 98% for two data series with agreeable error rate reductions.

Convolutional neural network-based hidden Markov models for rolling element bearing fault identification

A data indicator-based deep belief networks to detect multiple faults in axial piston pumps

Rolling element bearing fault detection using PPCA and spectral kurtosis

Crack detection in a shaft by combination of wavelet-based elements and genetic algorithm

The fault diagnosis of axial piston pumps is of significance for enhancing the reliability and security of hydraulic systems. Most of the faults occurring in the mechanical components of piston pumps are exhibited as fault-excited impulses. However, the strong impact-induced natural periodic impulses under the common working conditions (i.e. reciprocating motion of pistons) inevitably cause interference that considerably affects the fault detection performance. In this study, a simulation-determined band pass filter is employed to improve the performance of minimum entropy deconvolution (MED) for the fault diagnosis of axial piston pump bearings. First, a finite element method (FEM) simulation is performed to determine the possible carrier frequency. Second, the carrier frequency is used as the center frequency in association with a fixed bandwidth to determine the band pass filter parameters. Finally, the MED technique is applied to enhance weak fault-excited impulses by means of kurtosis maximization. Thereafter, envelope spectrum analysis is applied to the enhanced signals to obtain faulty feature frequencies. Two case studies are conducted, using bearings with faults in the outer and inner races of an axial piston pumps under common working conditions. The case studies confirm the necessity and effectiveness of the proposed method for detecting bearings faults in axial piston pumps.

Yongteng Zhong

Papers

Convolutional neural network-based hidden Markov models for rolling element bearing fault identification

A data indicator-based deep belief networks to detect multiple faults in axial piston pumps

Rolling element bearing fault detection using PPCA and spectral kurtosis

Crack detection in a shaft by combination of wavelet-based elements and genetic algorithm

Minimum entropy deconvolution based on simulation-determined band pass filter to detect faults in axial piston pump bearings.