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Applied Sciences
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Machine Learning and Signal Processing for Diagnostics and Prognostics Applications
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Machine Learning and Signal Processing for Diagnostics and Prognostics Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 August 2021) | Viewed by 27959

Special Issue Editor

Dr. Francesco Aggogeri

E-Mail Website
Guest Editor
Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38, 25123 Brescia, Italy
Interests: robotics; industrial automation; applied mechanics; diagnostics and prognostics modeling; machine learning strategies; robust design methodologies; technologies and manufacturing systems

Special Issue Information

Dear Colleagues,

Global industry competition is driving the maximization of the use of resources in terms of equipment, systems, and robots. Methodologies for modeling diagnostics and prognostics have played a crucial role in increasing reliability and durability of systems (e.g., robots, machine tools) based on a robust design and an effective selection of the features that need to be monitored when the equipment is installed in the operating conditions, avoiding breakdowns, failures, or malfunctions. The increase in data availability using machine learning strategies and signal processing has permitted the use of computational methods to extract information from data modeling diagnostics and prognostics and prevent undesired performance. In this way, many challenges and opportunities remain to develop novel models and approaches transferring research results and new knowledge for different applications.

The purpose of this Special Issue is to gather high-quality research and contributions that address recent advances and developments in machine learning strategies and signal processing for diagnostics and prognostics applications, focusing on innovative systems, robots, mechatronic devices, and industrial processes.

Prof. Francesco Aggogeri
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

Keywords

  • Fault detection, diagnostics, and prognostics (data-driven, model-based, and hybrid methods)
  • Machine learning strategies and applications
  • Robust design methodologies
  • Condition monitoring and sensors placement and optimization
  • Remaining useful life (RUL) prediction
  • Advanced computation and simulation methods
  • Medical prognosis based on machine learning approaches
  • Decision making strategies based on simulations and data-driven methods
  • Sensor data analysis and validation
  • Robot and mechatronic system diagnostics and prognostics
  • Industrial and healthcare applications

Published Papers (6 papers)

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Research

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17 pages, 7647 KiB  
Article
A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV
by Ke Zheng, Guozhu Jia, Linchao Yang and Jiaqing Wang
Appl. Sci. 2021, 11(12), 5410; https://0-doi-org.brum.beds.ac.uk/10.3390/app11125410 - 10 Jun 2021
Cited by 8 | Viewed by 1957
Abstract
In the process of Unmanned Aerial Vehicle (UAV) flight testing, plenty of compound faults exist, which could be composed of concurrent single faults or over-limit states alarmed by Built-In-Test (BIT) equipment. At present, there still lacks a suitable automatic labeling approach for UAV [...] Read more.
In the process of Unmanned Aerial Vehicle (UAV) flight testing, plenty of compound faults exist, which could be composed of concurrent single faults or over-limit states alarmed by Built-In-Test (BIT) equipment. At present, there still lacks a suitable automatic labeling approach for UAV flight data, effectively utilizing the information of the BIT record. The performance of the originally employed flight data-driven fault diagnosis models based on machine learning needs to be improved as well. A compound fault labeling and diagnosis method based on actual flight data and the BIT record of the UAV during flight test phase is proposed, through labeling the flight data with compound fault modes corresponding to concurrent single faults recorded by the BIT system, and upgrading the original diagnosis model based on Gradient Boosting Decision Tree (GBDT) and Fully Convolutional Network (FCNN), to eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and modified Convolutional Neural Network (CNN). The experimental results based on actual test flight data show that the proposed method could effectively label the flight data and obtain a significant improvement in diagnostic performance, appearing to be practical in the UAV test flight process. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for Diagnostics and Prognostics Applications)
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16 pages, 4564 KiB  
Article
Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments
by Seokju Oh, Seugmin Han and Jongpil Jeong
Appl. Sci. 2021, 11(9), 3963; https://0-doi-org.brum.beds.ac.uk/10.3390/app11093963 - 27 Apr 2021
Cited by 16 | Viewed by 2306
Abstract
The failure of a facility to produce a product can have significant impacts on the quality of the product. Most equipment failures occur in rotating equipment, with bearing damage being the biggest cause of failure in rotating equipment. In this paper, we propose [...] Read more.
The failure of a facility to produce a product can have significant impacts on the quality of the product. Most equipment failures occur in rotating equipment, with bearing damage being the biggest cause of failure in rotating equipment. In this paper, we propose a denoising autoencoder (DAE) and multi-scale convolution recurrent neural network (MS-CRNN), wherein the DAE accurately inspects bearing defects in the same environment as bearing vibration signals in the field, and the MS-CRNN inspects and classifies defects. We experimented with adding random noise to create a dataset that resembled noisy manufacturing installations in the field. From the results of the experiment, the accuracy of the proposed method was more than 90%, proving that it is an algorithm that can be applied in the field. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for Diagnostics and Prognostics Applications)
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20 pages, 4294 KiB  
Article
Fault Diagnosis of a Reciprocating Compressor Air Valve Based on Deep Learning
by Shungen Xiao, Ang Nie, Zexiong Zhang, Shulin Liu, Mengmeng Song and Hongli Zhang
Appl. Sci. 2020, 10(18), 6596; https://0-doi-org.brum.beds.ac.uk/10.3390/app10186596 - 21 Sep 2020
Cited by 20 | Viewed by 3575
Abstract
With the development of machine learning in recent years, the application of machine learning to machine fault diagnosis has become increasingly popular. Applying traditional feature extraction methods for complex systems will weaken the characterization capacity of features, which are not conducive to subsequent [...] Read more.
With the development of machine learning in recent years, the application of machine learning to machine fault diagnosis has become increasingly popular. Applying traditional feature extraction methods for complex systems will weaken the characterization capacity of features, which are not conducive to subsequent classification work. A reciprocating compressor is a complex system. In order to improve the fault diagnosis accuracy of complex systems, this paper does not use traditional fault diagnosis methods and applies deep convolutional neural networks (CNNs) to process this nonlinear and non-stationary fault signal. The valve fault data is obtained from the reciprocating compressor test bench of the Daqing Natural Gas Company. Firstly, the single-channel vibration signal is collected on the reciprocating compressor and the one-dimensional CNN (1-D CNN) is used for fault diagnosis and compared with the traditional model to verify the effectiveness of the 1-D CNN. Next, the collected eight channels signals (three channels of vibration signals, four channels of pressure signals, one channel key phase signal) are applied by 1-D CNN and 2-D CNN for fault diagnosis to verify the CNN that it is still suitable for multi-channel signal processing. Finally, further study on the influence of the input of different channel signal combinations on the model diagnosis accuracy is carried out. Experiments show that the seven-channel signal (three-channel vibration signal, four-channel pressure signal) with the key phase signal removed has the highest diagnostic accuracy in the 2-D CNN. Therefore, proper deletion of useless channels can not only speed up network operations but also improve diagnosis accuracy. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for Diagnostics and Prognostics Applications)
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13 pages, 1944 KiB  
Article
Inspection and Classification of Semiconductor Wafer Surface Defects Using CNN Deep Learning Networks
by Jong-Chih Chien, Ming-Tao Wu and Jiann-Der Lee
Appl. Sci. 2020, 10(15), 5340; https://0-doi-org.brum.beds.ac.uk/10.3390/app10155340 - 02 Aug 2020
Cited by 61 | Viewed by 12798
Abstract
Due to advances in semiconductor processing technologies, each slice of a semiconductor is becoming denser and more complex, which can increase the number of surface defects. These defects should be caught early and correctly classified in order help identify the causes of these [...] Read more.
Due to advances in semiconductor processing technologies, each slice of a semiconductor is becoming denser and more complex, which can increase the number of surface defects. These defects should be caught early and correctly classified in order help identify the causes of these defects in the process and eventually help to improve the yield. In today’s semiconductor industry, visible surface defects are still being inspected manually, which may result in erroneous classification when the inspectors become tired or lose objectivity. This paper presents a vision-based machine-learning-based method to classify visible surface defects on semiconductor wafers. The proposed method uses deep learning convolutional neural networks to identify and classify four types of surface defects: center, local, random, and scrape. Experiments were performed to determine its accuracy. The experimental results showed that this method alone, without additional refinement, could reach a top accuracy in the range of 98% to 99%. Its performance in wafer-defect classification shows superior performance compared to other machine-learning methods investigated in the experiments. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for Diagnostics and Prognostics Applications)
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11 pages, 2352 KiB  
Article
A Fault Feature Extraction Method Based on Second-Order Coupled Step-Varying Stochastic Resonance for Rolling Bearings
by Lu Lu, Yu Yuan, Chen Chen and Wu Deng
Appl. Sci. 2020, 10(7), 2602; https://0-doi-org.brum.beds.ac.uk/10.3390/app10072602 - 10 Apr 2020
Cited by 2 | Viewed by 1802
Abstract
In mechanical equipment, rolling bearings analyze and monitor their fault based on their vibration signals. Vibration signals obtained are usually weak because the machine works in a noisy background that makes it very difficult to extract its feature. To address this problem, a [...] Read more.
In mechanical equipment, rolling bearings analyze and monitor their fault based on their vibration signals. Vibration signals obtained are usually weak because the machine works in a noisy background that makes it very difficult to extract its feature. To address this problem, a second-order coupled step-varying stochastic resonance (SCSSR) system is proposed. The system couples two second-order stochastic resonance (SR) systems into a multistable system, one of which is a controlled system and the other of which is a controlling system that uses the output of one system to adjust the output of the other system to enhance the weak signal. In this method, we apply the seeker optimization algorithm (SOA), which uses the output signal-to-noise ratio (SNR) as the estimating function and combines the twice-sampling technology to adaptively select the parameters of the coupled SR system to achieve feature enhancement and collection of the weak periodic signal. The simulation and real fault data of a bearing prove that this method has better results in detecting weak signals, and the system output SNR is higher than the traditional SR method. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for Diagnostics and Prognostics Applications)
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Review

Jump to: Research

27 pages, 373 KiB  
Review
Recent Advances on Machine Learning Applications in Machining Processes
by Francesco Aggogeri, Nicola Pellegrini and Franco Luis Tagliani
Appl. Sci. 2021, 11(18), 8764; https://0-doi-org.brum.beds.ac.uk/10.3390/app11188764 - 21 Sep 2021
Cited by 23 | Viewed by 4427
Abstract
This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance in terms of production [...] Read more.
This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for Diagnostics and Prognostics Applications)
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