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Machine Learning from Heterogeneous Condition Monitoring Sensor Data for Predictive Maintenance and Smart Industry

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

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 41135

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Special Issue Editors

Prof. Dr. Grzegorz J. Nalepa

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Guest Editor

Institute of Applied Computer Science, Jagiellonian Univeristy, 31-007 Krakow, Poland
Interests: artificial intelligence; knowledge engineering; affective computing; explainability
Special Issues, Collections and Topics in MDPI journals

Dr. João Gama

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Guest Editor

Faculty of Economics, University of Porto, Porto, Portugal
Interests: learning from data streams; novelty detection; social network analysis
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Dr. Olga Fink

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Guest Editor

ETH Zurich, Switzerland
Interests: intelligent maintenance systems, data‐driven condition‐based and predictive maintenance, hybrid approaches fusing physical performance models and deep learning algorithms

Dr. Albert Bifet

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Télécom Paris, Palaiseau, France
Interests: online analytics; stream mining; massive online analytics
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Prof. Dr. David Camacho

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Guest Editor

Universidad Politécnica de Madrid, Spain
Interests: machine learning, evolutionary computation, swarm intelligence, social network analysis

Special Issue Information

Dear colleagues,

Smart Industry relies on the advanced use of sensor technology as well as the use of data mining techniques based on machine learning algorithms. In fact, machine learning and deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding. The drivers for this vibrant development have been the availability of abundant data, breakthroughs of algorithms, and advancements in hardware. Recently, complex industrial assets have been extensively monitored by intelligent sensors and large amounts of heterogeneous condition monitoring signals have been collected. However, the application of machine learning approaches in the intelligent maintenance and operation of complex industrial assets so far has been limited. This Special Issue aims at shedding light into the current developments, drivers, challenges, potential solutions, and future research needs in the fields of the use and analysis of heterogeneous condition monitoring sensor data in smart industries, as well as industrial artificial intelligence applied to the intelligent maintenance and operation of complex industrial assets.

Authors of selected high-qualified papers from the 21st International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) welcome to submit extended versions of their original papers (50% extensions of the contents of the conference paper) and contributions.

The topics of the Special Issue include but are not limited to the following:

Sensor technology in smart industry applications;
Analysis of heterogeneous condition monitoring sensor data;
Fault Detection and Diagnosis (FDD);
Estimation of remaining useful life of components and machines;
Early failure and anomaly detection and analysis;
Predictive and prescriptive maintenance;
Hybrid approaches combining physics-based with data-driven approaches;
Self-healing and self-correction;
Self-adaptive time-series-based models for prognostics and forecasting;
Concept drift issues in dynamic predictive maintenance systems;
Active learning and Design of Experiment (DoE) in dynamic predictive maintenance;
Industrial process monitoring and modelling;
Activity recognition in the industrial setting;
Event logs abstraction methods and anomaly detection;
Conformance checking of industrial process models;
Network analysis on event log data;
Supervised and unsupervised methods of log analysis;
Machine learning and deep learning methods in smart industries;
Explainable AI for predictive maintenance.

Prof. Dr. Grzegorz J. Nalepa
Dr. João Gama
Dr. Olga Fink
Dr. Albert Bifet
Prof. Dr. David Camacho
Guest Editors

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Published Papers (8 papers)

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Research

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15 pages, 1434 KiB

Open AccessArticle

Attention Horizon as a Predictor for the Fuel Consumption Rate of Drivers

by Hamid Sarmadi, Sławomir Nowaczyk, Rune Prytz and Miguel Simão

Sensors 2022, 22(6), 2301; https://doi.org/10.3390/s22062301 - 16 Mar 2022

Viewed by 2490

Abstract

Understanding the operation of complex assets such heavy-duty vehicles is essential for improving the efficiency, sustainability, and safety of future industry. Specifically, reducing energy consumption of transportation is crucially important for fleet operators, due to the impact it has on decreasing energy costs and lowering greenhouse gas emissions. Drivers have a high influence on fuel usage. However, reliably estimating driver performance is challenging. This is a key component of many eco-driving tools used to train drivers. Some key aspects of good, or efficient, drivers include being more aware of the surroundings, adapting to the road situations, and anticipating likely developments of the traffic conditions. With the development of IoT technologies and possibility of collecting high-precision and high-frequency data, even such vague concepts can be qualitatively measured, or at least approximated. In this paper, we demonstrate how the driver’s degree of attention to the road can be automatically extracted from onboard sensor data. More specifically, our main contribution is introduction of a new metric, called attention horizon (AH); it can, fully automatically and based on readily-available IoT data, capture, differentiate, and evaluate a driver’s behavior as the vehicle approaches a red traffic light. We suggest that our measure encapsulates complex concepts such as driver’s “awareness” and “carefulness” in itself. This metric is extracted from the pedal positions in a 150 m trajectory just before stopping. We demonstrate that this metric is correlated with normalized fuel consumption rate (FCR) in the long term, making it a suitable tool for ranking and evaluating drivers. For example, over weekly periods we found a negative median correlation between AH and FCR with the absolute value of

0.156

; while using monthly data, the value was

0.402

. Full article

(This article belongs to the Special Issue Machine Learning from Heterogeneous Condition Monitoring Sensor Data for Predictive Maintenance and Smart Industry)

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20 pages, 3867 KiB

Open AccessArticle

Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations

by Jakub Jakubowski, Przemysław Stanisz, Szymon Bobek and Grzegorz J. Nalepa

Sensors 2022, 22(1), 291; https://doi.org/10.3390/s22010291 - 31 Dec 2021

Cited by 26 | Viewed by 4693

Abstract

Development of predictive maintenance (PdM) solutions is one of the key aspects of Industry 4.0. In recent years, more attention has been paid to data-driven techniques, which use machine learning to monitor the health of an industrial asset. The major issue in the implementation of PdM models is a lack of good quality labelled data. In the paper we present how unsupervised learning using a variational autoencoder may be used to monitor the wear of rolls in a hot strip mill, a part of a steel-making site. As an additional benchmark we use a simulated turbofan engine data set provided by NASA. We also use explainability methods in order to understand the model’s predictions. The results show that the variational autoencoder slightly outperforms the base autoencoder architecture in anomaly detection tasks. However, its performance on the real use-case does not make it a production-ready solution for industry and should be a matter of further research. Furthermore, the information obtained from the explainability model can increase the reliability of the proposed artificial intelligence-based solution. Full article

(This article belongs to the Special Issue Machine Learning from Heterogeneous Condition Monitoring Sensor Data for Predictive Maintenance and Smart Industry)

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26 pages, 10956 KiB

Open AccessArticle

Sensor-Based Predictive Maintenance with Reduction of False Alarms—A Case Study in Heavy Industry

by Marek Hermansa, Michał Kozielski, Marcin Michalak, Krzysztof Szczyrba, Łukasz Wróbel and Marek Sikora

Sensors 2022, 22(1), 226; https://doi.org/10.3390/s22010226 - 29 Dec 2021

Cited by 14 | Viewed by 4506

Abstract

In this paper, the problem of the identification of undesirable events is discussed. Such events can be poorly represented in the historical data, and it is predominantly impossible to learn from past examples. The discussed issue is considered in the work in the context of two use cases in which vibration and temperature measurements collected by wireless sensors are analysed. These use cases include crushers at a coal-fired power plant and gantries in a steelworks converter. The awareness, resulting from the cooperation with industry, of the need for a system that works in cold start conditions and does not flood the machine operator with alarms was the motivation for proposing a new predictive maintenance method. The proposed solution is based on the methods of outlier identification. These methods are applied to the collected data that was transformed into a multidimensional feature vector. The novelty of the proposed solution stems from the creation of a methodology for the reduction of false positive alarms, which was applied to a system identifying undesirable events. This methodology is based on the adaptation of the system to the analysed data, the interaction with the dispatcher, and the use of the XAI (eXplainable Artificial Intelligence) method. The experiments performed on several data sets showed that the proposed method reduced false alarms by 90.25% on average in relation to the performance of the stand-alone outlier detection method. The obtained results allowed for the implementation of the developed method to a system operating in a real industrial facility. The conducted research may be valuable for systems with a cold start problem where frequent alarms can lead to discouragement and disregard for the system by the user. Full article

(This article belongs to the Special Issue Machine Learning from Heterogeneous Condition Monitoring Sensor Data for Predictive Maintenance and Smart Industry)

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27 pages, 4098 KiB

Open AccessArticle

Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework

by Sarvesh Sundaram and Abe Zeid

Sensors 2021, 21(18), 5994; https://doi.org/10.3390/s21185994 - 7 Sep 2021

Cited by 19 | Viewed by 4621

Abstract

Advances in the manufacturing industry have led to modern approaches such as Industry 4.0, Cyber-Physical Systems, Smart Manufacturing (SM) and Digital Twins. The traditional manufacturing architecture that consisted of hierarchical layers has evolved into a hierarchy-free network in which all the areas of a manufacturing enterprise are interconnected. The field devices on the shop floor generate large amounts of data that can be useful for maintenance planning. Prognostics and Health Management (PHM) approaches use this data and help us in fault detection and Remaining Useful Life (RUL) estimation. Although there is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of PHM. This paper conducts a review of PHM approaches, the current research trends and proposes a three-phased interoperable framework to implement Smart Prognostics and Health Management (SPHM). The uniqueness of SPHM lies in its framework, which makes it applicable to any manufacturing operation across the industry. The framework consists of three phases: Phase 1 consists of the shopfloor setup and data acquisition steps, Phase 2 describes steps to prepare and analyze the data and Phase 3 consists of modeling, predictions and deployment. The first two phases of SPHM are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research. As a use-case, the first two phases of the SPHM framework are applied to data from a milling machine operation. Full article

(This article belongs to the Special Issue Machine Learning from Heterogeneous Condition Monitoring Sensor Data for Predictive Maintenance and Smart Industry)

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24 pages, 4298 KiB

Open AccessArticle

An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines

by Ruifeng Cao and Akilu Yunusa-Kaltungo

Sensors 2021, 21(9), 2957; https://doi.org/10.3390/s21092957 - 23 Apr 2021

Cited by 23 | Viewed by 2725

Abstract

The feasibility and usefulness of frequency domain fusion of data from multiple vibration sensors installed on typical industrial rotating machines, based on coherent composite spectrum (CCS) as well as poly-coherent composite spectrum (pCCS) techniques, have been well-iterated by earlier studies. However, all previous endeavours have been limited to rotor faults, thereby raising questions about the proficiency of the approach for classifying faults related to other critical rotating machine components such as gearboxes. Besides the restriction in scope of the founding CCS and pCCS studies on rotor-related faults, their diagnosis approach was manually implemented, which could be unrealistic when faced with routine condition monitoring of multi-component industrial rotating machines, which often entails high-frequency sampling at multiple locations. In order to alleviate these challenges, this paper introduced an automated framework that encompassed feature generation through CCS, data dimensionality reduction through principal component analysis (PCA), and faults classification using artificial neural network (ANN). The outcomes of the automated approach are a set of visualised decision maps representing individually simulated scenarios, which simplifies and illustrates the decision rules of the faults characterisation framework. Additionally, the proposed approach minimises diagnosis-related downtime by allowing asset operators to easily identify anomalies at their incipient stages without necessarily possessing vibration monitoring expertise. Building upon the encouraging results obtained from the preceding part of this approach that was limited to well-known rotor-related faults, the proposed framework was significantly extended to include experimental and open-source gear fault data. The results show that in addition to early established rotor-related faults classification, the approach described here can also effectively and automatically classify gearbox faults, thereby improving the robustness. Full article

(This article belongs to the Special Issue Machine Learning from Heterogeneous Condition Monitoring Sensor Data for Predictive Maintenance and Smart Industry)

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14 pages, 769 KiB

Open AccessArticle

Interpretable Detection of Partial Discharge in Power Lines with Deep Learning

by Gabriel Michau, Chi-Ching Hsu and Olga Fink

Sensors 2021, 21(6), 2154; https://doi.org/10.3390/s21062154 - 19 Mar 2021

Cited by 18 | Viewed by 3314

Abstract

Partial discharge (PD) is a common indication of faults in power systems, such as generators and cables. These PDs can eventually result in costly repairs and substantial power outages. PD detection traditionally relies on hand-crafted features and domain expertise to identify very specific pulses in the electrical current, and the performance declines in the presence of noise or of superposed pulses. In this paper, we propose a novel end-to-end framework based on convolutional neural networks. The framework has two contributions: First, it does not require any feature extraction and enables robust PD detection. Second, we devise the pulse activation map. It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs. The performance is evaluated on a public dataset for the detection of damaged power lines. An ablation study demonstrates the benefits of each part of the proposed framework. Full article

(This article belongs to the Special Issue Machine Learning from Heterogeneous Condition Monitoring Sensor Data for Predictive Maintenance and Smart Industry)

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22 pages, 5056 KiB

Open AccessArticle

Predictive Maintenance Scheduling with Failure Rate Described by Truncated Normal Distribution

by Iwona Paprocka, Wojciech M. Kempa and Grzegorz Ćwikła

Sensors 2020, 20(23), 6787; https://doi.org/10.3390/s20236787 - 27 Nov 2020

Cited by 5 | Viewed by 2640

Abstract

The method of risk assessment and planning of technical inspections of machines and optimization of production tasks is the main focus of this study. Any unpredicted failure resulted in the production plans no longer being valid, production processes needing to be rescheduled, costs of unused machine production capacity and losses due to the production of poor-quality products increase, as well as additional costs of human resources, equipment, and materials used during the maintenance. The method reflects the operation of the production system and the nature of the disturbances, allowing for the estimation of unknown parameters related to machine reliability. The machine failure frequency was described with the normal distribution truncated to the positive half of the axis. In production practice, this distribution is commonly used to describe the phenomenon of irregularities. The presented method was an extension of the Six Sigma concept for monitoring and continuous control in order to eliminate and prevent various inconsistencies in processes and resulting products. Reliability characteristics were used to develop predictive schedules. Schedules were assessed using the criteria of solution and quality robustness. Estimation methods of parameters describing disturbances were compared for different job shop scheduling problems. The estimation method based on a maximum likelihood approach allowed for more accurate prediction of scheduling problems. The paper presents a practical example of the application of the proposed method for electric steering gears. Full article

(This article belongs to the Special Issue Machine Learning from Heterogeneous Condition Monitoring Sensor Data for Predictive Maintenance and Smart Industry)

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Review

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22 pages, 392 KiB

Open AccessReview

A Survey on Data-Driven Predictive Maintenance for the Railway Industry

by Narjes Davari, Bruno Veloso, Gustavo de Assis Costa, Pedro Mota Pereira, Rita P. Ribeiro and João Gama

Sensors 2021, 21(17), 5739; https://doi.org/10.3390/s21175739 - 26 Aug 2021

Cited by 62 | Viewed by 13236

Abstract

In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events—anomaly detection in time-series—can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research. Full article

(This article belongs to the Special Issue Machine Learning from Heterogeneous Condition Monitoring Sensor Data for Predictive Maintenance and Smart Industry)

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