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Online Supervision of Engineering Systems
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Online Supervision of Engineering Systems

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

Deadline for manuscript submissions: closed (10 December 2021) | Viewed by 4870

Special Issue Editor

Prof. Dr. Belkacem Ould-Bouamama

E-Mail Website
Guest Editor
UMR 9189, CRIStAL, Université de Lille, 59650 Villeneuve d’Ascq, France
Interests: process engineering; renewable energies; mechatronic systems; hydrogen engine; fuel cell; multi-source systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Typically, most fault diagnosis developed in industry are done offline using Failures Modes and Effects Analysis (FMEA) or fault trees when there was sufficient degradation or a breakdown in the plant. However, technological systems have become more complex and safety-critical, the needs for including automated monitoring and diagnosis as part of the intelligent control loop has become critical. The need for process safety under a wide variety of operating conditions essentially requires online supervision including fault detection and isolation (FDI) procedures (model or data-based methods) that can inform intelligent Fault Tolerant and Fault Adaptive control (FTC and FAC). However, process supervision used in industry is essentially based on fixed thresholds of the raw measurements of variables or a material and sensor redundancies. This is insufficient to predict a drift of the process variables and alarm management isolating a faulty component or phenomena.

Widely developed online FDI algorithms in the literature are tested in simulation. The complexity in real application is due of accuracy and robustness of dynamic model, non-stationarity of the process to be monitored, presence of noises, difficulty to collect faulty modes, need of training of operators to a new technology etc. This is why, contrary to industrial control, FDI applications are not yet well developed.

This special issue deals with real (or pilot) innovative industrial applications of online FDI and FTC algorithms. The goal is to exchange the industrial and fundamental latest technological developments in this area. We invite researchers and industrials to contribute their original research or review articles to this special issue.

Prof. Dr. Belkacem Ould-Bouamama
Guest Editor

Manuscript Submission Information

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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

  • supervision
  • diagnosis
  • fault tolerant control
  • data based FDI
  • model based FDI
  • AI for predictive maintenance
  • PHM

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Published Papers (2 papers)

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Research

18 pages, 3737 KiB  
Article
A Study on PF–IFF-Based Diagnosis Model of Plant Equipment Failure
by Min-Young Seo, Se-Yun Hwang, Jang-Hyun Lee, Jae-Gon Kim and Hong-Bae Jun
Appl. Sci. 2022, 12(1), 347; https://doi.org/10.3390/app12010347 - 30 Dec 2021
Viewed by 1697
Abstract
There are two types of maintenance policies for equipment: breakdown maintenance and preventive maintenance. In the case of applying preventive maintenance, the maintenance is carried out based on time or the condition of the equipment. However, with the development of Information and Communications [...] Read more.
There are two types of maintenance policies for equipment: breakdown maintenance and preventive maintenance. In the case of applying preventive maintenance, the maintenance is carried out based on time or the condition of the equipment. However, with the development of Information and Communications Technologies (ICT) and the Internet of Things (IoT) technology, the data collected from equipment has rapidly increased and the use of Condition-Based Maintenance (CBM) to perform appropriate maintenance based on the condition of the equipment is increasing. In this study, based on gathered sensor data, we introduce an approach to diagnosing the condition of the equipment by extracting specific data features related to the types of failures that occur with equipment. To this end, we used the K-means clustering method, support vector machine (SVM) classifier, and Pattern Frequency–Inverse Failure mode Frequency (PF–IFF) method with the Term Frequency–Inverse Document Frequency (TF–IDF) method. As a case study, we applied the proposed approach to a centrifugal pump and carried out computational experiments for assessing the performance and validity of the proposed approach. Full article
(This article belongs to the Special Issue Online Supervision of Engineering Systems)
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25 pages, 5208 KiB  
Article
Research on Prediction Method of Hydraulic Pump Remaining Useful Life Based on KPCA and JITL
by Zhenbao Li, Wanlu Jiang, Sheng Zhang, Decai Xue and Shuqing Zhang
Appl. Sci. 2021, 11(20), 9389; https://doi.org/10.3390/app11209389 - 10 Oct 2021
Cited by 19 | Viewed by 2626
Abstract
Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just in time learning (JITL) method was proposed to solve this problem. First, as [...] Read more.
Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just in time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability. Full article
(This article belongs to the Special Issue Online Supervision of Engineering Systems)
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