An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

A comprehensive review on convolutional neural network in machine fault diagnosis

A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery

The task of condition monitoring and fault diagnosis of rolling element bearing is often cumbersome and labour intensive. Various techniques have been proposed for rolling bearing fault detection and diagnosis. The challenge however, is to efficiently and accurately extract features from signals acquired from these elements, particularly in the time–frequency domain. A new time–frequency technique, known as basis pursuit, was recently developed. This paper presents an application of this new basis pursuit method in the extraction of features from signals collected from faulty rolling bearings with inner race and outer race faults. Results obtained using this new technique were compared with discrete wavelet packet analysis (DWPA) and the matching pursuit technique. Basis pursuit represents features with very fine resolution and sparsity in the time–frequency domain thus rendering easier interpretation of the analysed results. The technique also improves the signal to noise ratio so that subsequent fault detection and identification can be conducted with confidence.

Fault diagnosis of rolling element bearings using basis pursuit

Parameter-adaptive variational mode decomposition (VMD) has attenuated the dominant effect of prior parameters, especially the predefined mode number and balancing parameter, which heavily trouble the traditional VMD. However, parameter-adaptive VMD still encounters some problems when it is applied to the data from industry applications. On one hand, the mode number chosen using parameter-adaptive VMD is not the optimal. Numbers of redundant modes are decomposed. On another hand, parameter-adaptive VMD has much space for the improvement when it is applied to compound-fault diagnosis. To solve these issues and further enhance its performance, an improved parameter-adaptive VMD (IPAVMD) is proposed in this paper. Firstly, a new index, called ensemble kurtosis, is constructed by combining with kurtosis and the envelope spectrum kurtosis. It can simultaneously take the cyclostationary and impulsiveness into consideration. Secondly, the optimization objective function of grasshopper optimization algorithm is improved based on the ensemble kurtosis. The improved method chooses the mean value of the ensemble kurtosis of all modes rather than that of the individual mode as objective function. Thirdly, to extract all potential fault information, an iteration algorithm is used in the new method. Benefiting from these improvements, the proposed IPAVMD outperforms the traditional parameter-adaptive VMD and further expands the application to compound-fault diagnosis. It has been verified by a series of simulated signals and a real dataset from the axle box bearings of locomotive.

Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition.

Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery

As more and more data become available for machine prognostic analysis in the big data environment, effective data suitability assessment methods become highly desired to help locate data with sufficient quality for analysis. Driven by this purpose, this paper proposes a novel and systematic methodology for data suitability assessment based on the needs of prognostics and health management (PHM). In this study, the data suitability for PHM is assessed from the aspects of detectability, diagnosability, and trendability, which correspond to the three major tasks of PHM: fault detection, fault diagnosis, and degradation assessment. The proposed methodology is mainly built upon the recent research studies on maximum mean discrepancy in the field of machine learning, which include a family of test statistics that are used to test the difference between two data distributions. The effectiveness of the proposed methodology is demonstrated in diverse industrial applications, which include semiconductors, boring tool degradation, and sensorless drive diagnosis. The results in the case studies indicate that the proposed methodology can be a promising tool to evaluate whether the data under study or the extracted feature set is suitable for PHM tasks.

Yuan Di

Papers

Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery

Assessment of Data Suitability for Machine Prognosis Using Maximum Mean Discrepancy

Adaptive virtual metrology for semiconductor chemical mechanical planarization process using GMDH-type polynomial neural networks

Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement

A geometrical investigation on the generalized lp/lq norm for blind deconvolution