Special Issue "Deep Learning, Artificial Neural Networks and Sensors for Fault Diagnosis"
Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 100892
Interests: rotordynamics; fault diagnostics; rolling element bearings; oil-film bearings
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Nowadays, fault diagnosis of mechanical components is a real challenging task in industrial field and is often performed by means of the analysis of sensor signals. Accelerometers, temperature and pressure probes are examples of typical sensors used in the diagnosis of a wide range of industrial machines. A lot of signal processing techniques have been developed in the years, from simple statistic indicators to more sophisticated tools often based on frequency analysis. The result of such analysis needs to be interpreted for the assessment of the type of the damage and its level of severity. The type of the damage is often detected by the occurrence of known features in the signal, whereas the severity of the fault is performed by tracking some damage indicators as a function of the machine running time. Deviation of such indicators from the nominal value of healthy case is used to classify the intensity of the damage.
In such activity, the application of artificial neural networks is become very popular in fault diagnosis, where the damage indicators and signal features are classified in an automatic way. The training phase is the critical part of the process and need the availability of data of healthy and damaged cases. The core of the algorithm is given by the connections between the layers of the network. The number of layers and connections gives the power of the network by replicating the connections of neurons in a human brain. Recently, the power of artificial neural networks increased with the number of layers and connections leading to the so-called deep neural networks. In this sense deep learning allows the training phase to be avoided.
The Special Issue “Deep Learning, Artificial Neural Networks and Sensors for Fault Diagnosis” aims to summarize the state of the art of the research of fault diagnosis of industrial machines and components by means of artificial neural networks and deep learning.
The purpose of the Special Issue is to collect original research papers or review articles. Although the emphasis is on practical applications, we also welcome fundamental studies.
Prof. Dr. Steven Chatterton
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Deep Learning
- Artificial Neural Networks
- Convolutional Neural Network
- Gears fault diagnosis
- Rolling element bearings diagnosis
- Machines fault diagnosis