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Achieving Predictive Maintenance using Sensors: Real or Fantasy?
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Achieving Predictive Maintenance using Sensors: Real or Fantasy?

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 December 2019) | Viewed by 23226

Special Issue Editors

Prof. Dr. Henrique Santos

E-Mail Website
Guest Editor
Information Systems Department, University of Minho, 4800-058 Guimarães, Portugal
Interests: information security and privacy; computer architecture
Special Issues, Collections and Topics in MDPI journals
Dr. Vítor J. Sá

E-Mail Website
Guest Editor
Universidade Católica Portuguesa, Braga, Portugal
Interests: biometric authentication; multimodal interaction; computer graphics; programming languages; project management

Special Issue Information

Dear Colleagues,

The increase of interest in smart sensors in recent years has created a revolution in a great number of areas, such as health monitoring, smart homes, or even smart cities, in large supported by the IoT concept (itself in permanent evolution). In common, most of the considered environments are structured around distributed sensors capable of capturing several variables linked to its operation. In this Special Issue, we are focusing on predictive maintenance, which could make life easier for everyone, as it will help to realize when a system or subsystem is going to start failing. With the use of sensors and proper data analysis models, any equipment could perform periodic condition monitoring, and using this data, along with the aid of machine learning techniques, it will be possible to foresee how the equipment is going to behave in the future. This can help, for example, to know about the status of pavement without having to be continuously monitoring it, or to know when a specific piece of a machine that costs thousands of dollars is going to fail, avoiding possible critical situations.

This Special Issue aims to explore the state-of-the-art in this topic, and also how far we are from reaching the real value of autonomous predictive maintenance.

Dr. Henrique Santos
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. Sensors 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 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.

Keywords

  • predictive maintenance
  • machine learning
  • internet of things
  • smart sensors
  • smart maintenance
  • Industry 4.0
  • failure models
  • sensor networks
  • performance metrics

Published Papers (4 papers)

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Research

18 pages, 6811 KiB  
Article
A Real-Time Fault Early Warning Method for a High-Speed EMU Axle Box Bearing
by Lei Liu, Dongli Song, Zilin Geng and Zejun Zheng
Sensors 2020, 20(3), 823; https://doi.org/10.3390/s20030823 - 04 Feb 2020
Cited by 20 | Viewed by 3180
Abstract
An axle box bearing is one of the most important components of high-speed EMUs (electric multiple units), which runs at a very fast speed, suffers a heavy load, and operates under various complex working conditions. Once a bearing fault occurs, it not only [...] Read more.
An axle box bearing is one of the most important components of high-speed EMUs (electric multiple units), which runs at a very fast speed, suffers a heavy load, and operates under various complex working conditions. Once a bearing fault occurs, it not only has an enormous impact on the railway system, but also poses a threat to personal safety. Therefore, there is significant value in studying a real-time fault early warning of a high-speed EMU axle box bearing. However, to our best knowledge, there are three obvious defects in the existing fault early warning methods used for high-speed EMU axle box bearings: (1) these methods based on vibration are extremely mature, but there are no vibration sensors installed in high-speed EMU axle box because it will greatly increase the manufacturing cost; (2) a TADS (trackside acoustic device system) can effectively detect early failures, but only a portion of railways are equipped with such a facility; and (3) an EMU-ODS (electric multiple unit onboard detection system) has reported numerous untimely warnings, along with warnings of frequent occurrence being missed. Whereupon, a method is proposed to realize the fault early warning of an axle box bearing without installing a vibration sensor on the high-speed EMU in service, namely a MLSTM-iForest (multilayer long short-term memory–isolation forest). First, the time-series data of the temperature-related variables of the axle box bearing is used as the input of MLSTM to predict the axle box bearing temperature in the future. Then, the deviation index of the predicted axle box bearing temperature is calculated. Finally, the deviation index is input into an iForest algorithm for unsupervised classification to realize the fault early warning of an axle box bearing. Experimental results on high-speed EMU operation data sets demonstrated the availability and feasibility of the presented method toward achieving early fault warnings of a high-speed EMU axle box bearing. Full article
(This article belongs to the Special Issue Achieving Predictive Maintenance using Sensors: Real or Fantasy?)
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21 pages, 1571 KiB  
Article
Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management
by Divish Rengasamy, Mina Jafari, Benjamin Rothwell, Xin Chen and Grazziela P. Figueredo
Sensors 2020, 20(3), 723; https://doi.org/10.3390/s20030723 - 28 Jan 2020
Cited by 61 | Viewed by 9330
Abstract
Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architecture. However, contributions regarding improvement of different aspects [...] Read more.
Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system’s prognostics and diagnostics without modifying the models’ architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions. Full article
(This article belongs to the Special Issue Achieving Predictive Maintenance using Sensors: Real or Fantasy?)
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21 pages, 3985 KiB  
Article
Development of an On-Board Measurement System for Railway Vehicle Wheel Flange Wear
by Pacifique Turabimana and Celestin Nkundineza
Sensors 2020, 20(1), 303; https://doi.org/10.3390/s20010303 - 06 Jan 2020
Cited by 20 | Viewed by 7237
Abstract
The maintenance of railway systems is critical for their safe operation. However some landscape geographical features force the track line to have sharp curves with small radii. Sharp curves are known to be the main source of wheel flange wear. The reduction of [...] Read more.
The maintenance of railway systems is critical for their safe operation. However some landscape geographical features force the track line to have sharp curves with small radii. Sharp curves are known to be the main source of wheel flange wear. The reduction of wheel flange thickness to an extreme level increases the probability of train accidents. To avoid the unsafe operation of a rail vehicle, it is important to stay continuously up to date on the status of the wheel flange thickness dimensions by using precise and accurate measurement tools. The wheel wear measurement tools that are based on laser and vision technology are quite expensive to implement in railway lines of developing countries. Alternatively significant measurement errors can result from using imprecise measurement tools such as the hand tools, which are currently utilized by the railway companies such as Addis Ababa Light Rail Transit Service (AALRTS). Thus, the objective of this research is to propose and test a new measurement tool that uses an inductive displacement sensor. The proposed system works in both static and dynamic state of the railway vehicle and it is able to save the historical records of the wheel flange thickness for further analysis. The measurement system is fixed on the bogie frame. The fixture was designed using dimensions of the bogie and wheelset structure of the trains currently used by AALRTS. Laboratory experiments and computer simulations for of the electronic system were carried out to assess the feasibility of the data acquisition and analysis method. The noises and unwanted signals due to the dynamics of the system are filtered out from the sensor readings. The results show that the implementation of the proposed measurement system can accurately measure the wheel flange wear. Also, the faulty track section can be identified using the system recorded data and the operational control center data. Full article
(This article belongs to the Special Issue Achieving Predictive Maintenance using Sensors: Real or Fantasy?)
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14 pages, 2948 KiB  
Article
Intelligent Fault-Diagnosis System for Acoustic Logging Tool Based on Multi-Technology Fusion
by Xiaolong Hao, Xiaodong Ju, Junqiang Lu, Baiyong Men and Jing Zhou
Sensors 2019, 19(15), 3273; https://doi.org/10.3390/s19153273 - 25 Jul 2019
Cited by 2 | Viewed by 2733
Abstract
To improve the performance of acoustic logging tool in detecting three-dimensional formation, larger and more complicated transducer arrays have been used, which will greatly increase the difficulty of fault diagnosis during tool assembly and maintenance. As a result, traditional passive diagnostic methods become [...] Read more.
To improve the performance of acoustic logging tool in detecting three-dimensional formation, larger and more complicated transducer arrays have been used, which will greatly increase the difficulty of fault diagnosis during tool assembly and maintenance. As a result, traditional passive diagnostic methods become inefficient, and very skilled assemblers and maintainers are required. In this study, fault-diagnosis requirement for the acoustic logging tool at different levels has been analyzed from the perspective of the tool designer. An intelligent fault-diagnosis system consisting of a master-slave hardware architecture and a systemic diagnosis strategy was developed. The hardware system is based on the embedded technology, while the diagnosis strategy is built upon fault-tree analysis and data-driven methods. Diagnostic practice shows that this intelligent system can achieve four levels of fault diagnosis for the acoustic logging tool: System, subsystem, circuit board, and component. This study provided a more rigorous and professional fault diagnosis during tool assembly and maintenance. It is expected that this proposed method would be of great help in achieving cost reduction and improving work efficiency. Full article
(This article belongs to the Special Issue Achieving Predictive Maintenance using Sensors: Real or Fantasy?)
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