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Complex Systems Reliability and Maintenance Optimal Management Using the PHM...
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Complex Systems Reliability and Maintenance Optimal Management Using the PHM Approach and Artificial Intelligence

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 January 2022) | Viewed by 10122

Special Issue Editors

Prof. Dr. Alberto Regattieri

E-Mail Website
Guest Editor
Department of Industrial Engineering, Bologna University, 40126 Bologna, Italy
Interests: analysis, planning, design, and optimization of production processes and technologies
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Matthias Klumpp

E-Mail Website
Guest Editor
Department of Production and Logistics, Georg-August-University of Göttingen, 37073 Göttingen, Germany
Interests: digital production, retail and logistics operations; sustainability in global supply chains; qualification and knowledge management in logistics; efficiency measurement/data envelopment analysis; artificial intelligence and human-computer-interaction (HCI)
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel Delgado-Prieto

E-Mail Website
Guest Editor
Automatic Control Department, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain
Interests: cyber-physical systems; fault detection and diagnosis methods; machine and deep learning; electrical machines and drives
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the theory and application of prognostic and health management (PHM) methodologies in industrial contexts. PHM finds application in several domains, including aerospace, automotive, transportation, and manufacturing. Due to the potential advantages of a predictive maintenance policy, in terms of cost savings and asset management, PHM is receiving a broad consensus among industries too. 

Despite encouraging results achieved by the several methods proposed in the literature, PHM has seen little adoption by industries because of practical issues that need to be addressed. The most relevant ones can be summarized as follows: 

  1. The number of labelled training data is limited, given the difficulties in getting data in all possible operating and faulty conditions;
  2. Both operating and environmental conditions continuously change over time, making it hard to apply a pre-built model to a similar component/system in a different working environment;
  3. The data are often collected from different sources, at different frequencies, and stored in several devices, making a time-consuming pre-processing necessary;
  4. The equipment generates raw data that must be analyzed to extract relevant information. The transfer and storage of a high amount of raw data is a time-consuming activity for companies.

On the other hand, enabling technologies, such as the Industrial Internet of Things and Edge-Cloud Computing, hold great potential for the implementation of predictive maintenance in industries and should be exploited to deal with the abovementioned issues.

The focus of this Special Issue is to provide a forum for PHM researchers and practitioners to discuss the applicability and challenges of semi-supervised, incremental, and transfer learning for industrial PHM applications. Papers describing both novel applications and related theory are encouraged, with a specific focus on streaming analysis that provides real-time feedback on the health condition of the assets. 

Prof. Alberto Regattieri
Prof. Matthias Klumpp
Prof. Miguel Delgado-Prieto
Guest Editors

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Keywords

We are soliciting papers on topics that include but are not limited to:
  • Applications of semi-supervised and incremental learning techniques for novelty detection and fault detection 
  • Incremental feature learning for industrial equipment signals 
  • Applications of PHM in IIoT contexts 
  • Degradation modeling of components operating in different operating conditions 
  • System-level prognostic 
  • Definition of requirements and challenges for the implementation of Predictive Maintenance in industries  Integration of predictive maintenance with preventive policies 
  • Cost–benefit analysis of predictive maintenance

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Published Papers (4 papers)

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Research

12 pages, 1828 KiB  
Article
Model-Free Adaptive Control Based on Fractional Input-Output Data Model
by Chidentree Treestayapun and Aldo Jonathan Muñoz-Vázquez
Appl. Sci. 2022, 12(21), 11168; https://doi.org/10.3390/app122111168 - 4 Nov 2022
Cited by 1 | Viewed by 1318
Abstract
Memory properties of fractional-order operators are considered for an input-output data model for highly uncertain nonlinear systems. The model arises by relating the fractional-order variation of the output to the fractional-order variation of the input; the instantaneous gain is computed through a fuzzy [...] Read more.
Memory properties of fractional-order operators are considered for an input-output data model for highly uncertain nonlinear systems. The model arises by relating the fractional-order variation of the output to the fractional-order variation of the input; the instantaneous gain is computed through a fuzzy inference network, whose output consequences are adapted online on a gradient descent rule. The fractional-order nature of the proposed model relaxes the stringent conditions on data-driven schemes, allowing instantaneous changes in the output signal with a null variation in the controller. The main contribution consists of taking advantage of the memory properties of fractional-order operators and the flexibility of fuzzy logic rules to construct a data-driven model for highly uncertain discrete-time nonlinear systems. The relevance of the proposed method is assessed through experiments in a real-world scenario. Full article
(This article belongs to the Special Issue Complex Systems Reliability and Maintenance Optimal Management Using the PHM Approach and Artificial Intelligence)
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19 pages, 1899 KiB  
Article
Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
by Francesco Del Buono, Francesca Calabrese, Andrea Baraldi, Matteo Paganelli and Francesco Guerra
Appl. Sci. 2022, 12(10), 4931; https://doi.org/10.3390/app12104931 - 13 May 2022
Cited by 15 | Viewed by 3009
Abstract
Predictive Maintenance (PdM) is the newest strategy for maintenance management in industrial contexts. It aims to predict the occurrence of a failure to minimize unexpected downtimes and maximize the useful life of components. In data-driven approaches, PdM makes use of Machine Learning (ML) [...] Read more.
Predictive Maintenance (PdM) is the newest strategy for maintenance management in industrial contexts. It aims to predict the occurrence of a failure to minimize unexpected downtimes and maximize the useful life of components. In data-driven approaches, PdM makes use of Machine Learning (ML) algorithms to extract relevant features from signals, identify and classify possible faults (diagnostics), and predict the components’ remaining useful life (prognostics). The major challenge lies in the high complexity of industrial plants, where both operational conditions change over time and a large number of unknown modes occur. A solution to this problem is offered by novelty detection, where a representation of the machinery normal operating state is learned and compared with online measurements to identify new operating conditions. In this paper, a systematic study of autoencoder-based methods for novelty detection is conducted. We introduce an architecture template, which includes a classification layer to detect and separate the operative conditions, and a localizer for identifying the most influencing signals. Four implementations, with different deep learning models, are described and used to evaluate the approach on data collected from a test rig. The evaluation shows the effectiveness of the architecture and that the autoencoders outperform the current baselines. Full article
(This article belongs to the Special Issue Complex Systems Reliability and Maintenance Optimal Management Using the PHM Approach and Artificial Intelligence)
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18 pages, 2453 KiB  
Article
Genetic Programming-Based Feature Construction for System Setting Recognition and Component-Level Prognostics
by Francesca Calabrese, Alberto Regattieri, Raffaele Piscitelli, Marco Bortolini and Francesco Gabriele Galizia
Appl. Sci. 2022, 12(9), 4749; https://doi.org/10.3390/app12094749 - 9 May 2022
Cited by 3 | Viewed by 1984
Abstract
Extracting representative feature sets from raw signals is crucial in Prognostics and Health Management (PHM) for components’ behavior understanding. The literature proposes various methods, including signal processing in the time, frequency, and time–frequency domains, feature selection, and unsupervised feature learning. An emerging task [...] Read more.
Extracting representative feature sets from raw signals is crucial in Prognostics and Health Management (PHM) for components’ behavior understanding. The literature proposes various methods, including signal processing in the time, frequency, and time–frequency domains, feature selection, and unsupervised feature learning. An emerging task in data science is Feature Construction (FC), which has the advantages of both feature selection and feature learning. In particular, the constructed features address a specific objective function without requiring a label during the construction process. Genetic Programming (GP) is a powerful tool to perform FC in the PHM context, as it allows to obtain distinct feature sets depending on the analysis goal, i.e., diagnostics and prognostics. This paper adopts GP to extract system-level features for machinery setting recognition and component-level features for prognostics. Three distinct fitness functions are considered for the GP training, which requires a set of statistical time-domain features as input. The methodology is applied to vibration signals extracted from a test rig during run-to-failure tests under different settings. The performances of constructed features are evaluated through the classification accuracy and the Remaining Useful Life (RUL) prediction error. Results demonstrate that GP-based features classify known and novel machinery operating conditions better than feature selection and learning methods. Full article
(This article belongs to the Special Issue Complex Systems Reliability and Maintenance Optimal Management Using the PHM Approach and Artificial Intelligence)
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18 pages, 2425 KiB  
Article
Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery
by Francesca Calabrese, Alberto Regattieri, Marco Bortolini, Francesco Gabriele Galizia and Lorenzo Visentini
Appl. Sci. 2021, 11(20), 9580; https://doi.org/10.3390/app11209580 - 14 Oct 2021
Cited by 18 | Viewed by 2753
Abstract
Given the strategic role that maintenance assumes in achieving profitability and competitiveness, many industries are dedicating many efforts and resources to improve their maintenance approaches. The concept of the Smart Factory and the possibility of highly connected plants enable the collection of massive [...] Read more.
Given the strategic role that maintenance assumes in achieving profitability and competitiveness, many industries are dedicating many efforts and resources to improve their maintenance approaches. The concept of the Smart Factory and the possibility of highly connected plants enable the collection of massive data that allow equipment to be monitored continuously and real-time feedback on their health status. The main issue met by industries is the lack of data corresponding to faulty conditions, due to environmental and safety issues that failed machinery might cause, besides the production loss and product quality issues. In this paper, a complete and easy-to-implement procedure for streaming fault diagnosis and novelty detection, using different Machine Learning techniques, is applied to an industrial machinery sub-system. The paper aims to offer useful guidelines to practitioners to choose the best solution for their systems, including a model hyperparameter optimization technique that supports the choice of the best model. Results indicate that the methodology is easy, fast, and accurate. Few training data guarantee a high accuracy and a high generalization ability of the classification models, while the integration of a classifier and an anomaly detector reduces the number of false alarms and the computational time. Full article
(This article belongs to the Special Issue Complex Systems Reliability and Maintenance Optimal Management Using the PHM Approach and Artificial Intelligence)
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