Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.

A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

Wavelets for fault diagnosis of rotary machines: A review with applications

Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis

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

Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

A novel feature extraction and selection scheme was proposed for hybrid fault diagnosis of gearbox based on S transform, non-negative matrix factorization (NMF), mutual information and multi-objective evolutionary algorithms. Time-frequency distributions of vibration signals, acquired from gearbox with different fault states, were obtained by S transform. Then non-negative matrix factorization (NMF) was employed to extract features from the time-frequency representations. Furthermore, a two stage feature selection approach combining filter and wrapper techniques based on mutual information and non-dominated sorting genetic algorithms II (NSGA-II) was presented to get a more compact feature subset for accurate classification of hybrid faults of gearbox. Eight fault states, including gear defects, bearing defects and combination of gear and bearing defects, were simulated on a single-stage gearbox to evaluated the proposed feature extraction and selection scheme. Four different classifiers were employed to incorporate with the presented techniques for classification. Performances of four classifiers with different feature subsets were compared. Results of the experiments have revealed that the proposed feature extraction and selection scheme demonstrate to be an effective and efficient tool for hybrid fault diagnosis of gearbox.

A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox

Feature extraction for rolling element bearing fault diagnosis utilizing generalized S transform and two-dimensional non-negative matrix factorization

This paper presents a novel signal processing scheme, named the weighted multi-scale morphological gradient filter (WMMG), for rolling element bearing fault detection. The WMMG can depress the noise at large scale and preserve the impulsive shape details at small scale. Both a simulated signal and vibration signals from a bearing test rig are employed to evaluate the performance of the proposed technique. The traditional envelope analysis and a multi-scale enveloping spectrogram algorithm combining continuous wavelet transform and envelope analysis (WT-EA) are also studied and compared with the presented WMMG. Experimental results have demonstrated the effectiveness of the WMMG to extract the impulsive components from the raw vibration signal with strong background noise. We also investigated the classification performance on identifying bearing faults based on the WMMG and statistical parameters with varied noise levels. Application results reveal that the WMMG achieves the same or better performance as EA and WT-EA. Meanwhile, the WMMG requires low computation cost and is very suitable for on-line condition monitoring of bearing operating states.

A weighted multi-scale morphological gradient filter for rolling element bearing fault detection.

Gear fault detection using multi-scale morphological filters

In this work, we present a novel classification scheme named fuzzy lattice classifier (FLC) based on the lattice framework and apply it to the bearing faults diagnosis problem. Different from the fuzzy lattice reasoning (FLR) model developed in literature, there is no need to tune any parameter and to compute the inclusion measure in the training procedure in our new FLC model. It can converge rapidly in a single pass through training patterns with a few induced rules. A series of experiments are conducted on five popular benchmark datasets and three bearing datasets to evaluate and compare the presented FLC with the FLR model as well as some other widely used classification methods. Experimental results indicate that the FLC yields a satisfactory classification performance with higher computation efficiency than other classifiers. It is very desirable to utilize the FLC scheme for on-line condition monitoring of bearings and other mechanical systems.

Shuang-shan Mi

Papers

A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox

Feature extraction for rolling element bearing fault diagnosis utilizing generalized S transform and two-dimensional non-negative matrix factorization

A weighted multi-scale morphological gradient filter for rolling element bearing fault detection.

Gear fault detection using multi-scale morphological filters

Fuzzy lattice classifier and its application to bearing fault diagnosis