Deep learning and its applications to machine health monitoring

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

In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) methods, including artificial neural network, principal component analysis, support vector machines, etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in DL algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

pdf/deep-learning-algorithms-for-bearing-fault-diagnosticsx-a-4n8rkcrgab.pdf

Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review

With the tremendous revival of artificial intelligence, predictive maintenance (PdM) based on data-driven methods has become the most effective solution to address smart manufacturing and industrial big data, especially for performing health perception (e.g., fault diagnosis and remaining life assessment). Moreover, because the existing PdM research is still in primary experimental stage, most works are conducted utilizing several open-datasets, and the combination with specific applications such as rotating machinery is especially rare. Hence, in this paper, we focus on data-driven methods for PdM, present a comprehensive survey on its applications, and attempt to provide graduate students, companies, and institutions with the preliminary understanding of the existing works recently published. Specifically, we first briefly introduce the PdM approach, illustrate our PdM scheme for automatic washing equipment , and demonstrate the challenges encountered when we conduct a PdM research. Second, we classify the specific industrial applications based on six algorithms of machine learning and deep learning (DL), and compare five performance metrics for each classification. Furthermore, the accuracy (a metric to evaluate the algorithm performance) of these PdM applications is analyzed in detail. There are some important conclusions: 1) the data used in the summarized literature are mostly from public datasets, such as case western reserve university (CWRU)/intelligent maintenance systems (IMS); and 2) in recent years, researchers seem to focus more on DL algorithms for PdM research. Finally, we summarize the common features regarding our surveyed PdM applications and discuss several potential directions.

Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey

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 novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

Rolling bearings usually work in tough conditions, which makes the collected vibration signals complex and the fault features weak. Hence, fault feature extraction methods for rolling bearings have become a research focus. In this paper, a new method termed optimal variational mode decomposition (VMD) is proposed to extract rolling bearing fault features. Firstly, since envelope entropy is very sensitive to fault signal features, envelope entropy is used as a fitness function, which is an objective function for the whale optimization algorithm (WOA). Secondly, the WOA has numerous merits, such as simple operation, fewer adjustment parameters and a strong ability for jumping out of the local optimum, and it is applied to the optimization of VMD. Finally, intrinsic mode function components are processed through a Teager energy operator. The proposed method is employed to analyze the experimental signal collected from rolling bearings. The comparison results show that the proposed method is more effective and demonstrates superiority over empirical mode decomposition, local mean decomposition and wavelet packet decomposition.

https://iopscience.iop.org/article/10.1088/1361-6501/ab0352/pdf

An optimal variational mode decomposition for rolling bearing fault feature extraction

Vibration signals acquired from rolling bearing usually are complex, and it is difficult to extract fault features from strong noise background. In this paper, a chirplet decomposition method based on genetic algorithm is proposed. The absolute value of the inner product of the vibration signal and the basis function of chirplet is constructed as the optimization object function, using the genetic algorithm to search the chirplet which is best matched with the analyzed signal. Then a series of linear combination of chirplet are obtained, by which the time-frequency domain characteristic of the analyzed signal are indicated. The results confirm that the chirplet based on the genetic algorithm is more effective in extracting fault feature from strong noise background than the adaptive chirplet.

Rolling Bearing Fault Feature Extraction Using Chirplet Decomposition Based on Genetic Algorithm

Fault feature extraction from vibration signals is an important topic for fault diagnosis in rolling element bearings. However, the vibration signals measured from rolling element bearings are usually complex, and impulse components are usually embedded in strong background noise. In this paper, a novel method using an optimal chirplet with hybrid particle swarm optimization is proposed. The inner product absolute value of the vibration signal and the chirplet basis function is used as the fitness function. By heuristically searching the optimal parameters of the chirplet basis function, the optimal chirplet is further improved to increase its analysis results. The proposed method is applied to analyze vibration signals collected from rolling element bearings, and the results confirm that the proposed method is more effective in extracting fault features from strong noise background than traditional methods.

https://iopscience.iop.org/article/10.1088/1361-6501/aad8e8/pdf

Rolling element bearing fault feature extraction using an optimal chirplet

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Papers

A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

An optimal variational mode decomposition for rolling bearing fault feature extraction

Rolling Bearing Fault Feature Extraction Using Chirplet Decomposition Based on Genetic Algorithm

Rolling element bearing fault feature extraction using an optimal chirplet