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

Deep learning and its applications to machine health monitoring

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

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

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification

Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine

A hybrid remaining useful life prognostic method for proton exchange membrane fuel cell

Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach

Chen Lu

Papers

Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification

Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine

A hybrid remaining useful life prognostic method for proton exchange membrane fuel cell

Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach