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Sensors for Machine Condition Monitoring, Diagnostics, Prognostics and Maintenance

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

Deadline for manuscript submissions: 25 August 2024 | Viewed by 6735

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Special Issue Editors

Prof. Dr. Janis Terpenny

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Department of Systems Engineering and Operations Research and the Department of Mechanical Engineering, George Mason University, Fairfax, VA 22030, USA
Interests: smart integrated systems and processes for manufactured products and systems and design education

Prof. Dr. Thomas Kurfess

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George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Interests: next generation secure digital manufacturing systems

Prof. Dr. Vittal Prabhu

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Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802, USA
Interests: sensing and modeling of processes for control and decision-making especially in manufacturing and service industries

Dr. Dazhong Wu

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Guest Editor

Department of Mechanical and Aerospace Engineering, University of Central Florida, 12760 Pegasus Drive, Orlando, FL 32816, USA
Interests: smart manufacturing; additive manufacturing; engineering design
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Special Issue Information

Dear Colleagues,

The focus of this Special Issue is on the application of both legacy and next generation sensors for machine monitoring, diagnostics, prognostics, and maintenance. The issue will also address advanced analytic techniques (e.g., physics based models, AI and ML) for machine monitoring if these topics are coupled with novel sensors concepts, designs and applications. Authors are encouraged to incorporate approaches that leverage high performance computing systems, as well as distributed sensor network operations at the edge, and in fog and clouds. Furthermore, of interest would be new decision-making enabled by sensing including real-time decision-making and operational policies. Research addressing both legacy operations (e.g., classical rotational systems) as well as new system types (e.g., renewable energy generation systems) are welcome.

Prof. Dr. Janis Terpenny
Prof. Dr. Thomas Kurfess
Prof. Dr. Vittal Prabhu
Dr. Dazhong Wu
Guest Editors

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

diagnostics
machine monitoring
prognostics
preventative and productive maintenance
condition-based maintenance
fault diagnosis

Published Papers (5 papers)

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Research

26 pages, 8852 KiB

Open AccessArticle

Monitoring Flow-Forming Processes Using Design of Experiments and a Machine Learning Approach Based on Randomized-Supervised Time Series Forest and Recursive Feature Elimination

by Leroy Anozie, Bodo Fink, Christoph M. Friedrich and Christoph Engels

Sensors 2024, 24(5), 1527; https://doi.org/10.3390/s24051527 - 27 Feb 2024

Viewed by 510

Abstract

The machines of WF Maschinenbau process metal blanks into various workpieces using so-called flow-forming processes. The quality of these workpieces depends largely on the quality of the blanks and the condition of the machine. This creates an urgent need for automated monitoring of the forming processes and the condition of the machine. Since the complexity of the flow-forming processes makes physical modeling impossible, the present work deals with data-driven modeling using machine learning algorithms. The main contributions of this work lie in showcasing the feasibility of utilizing machine learning and sensor data to monitor flow-forming processes, along with developing a practical approach for this purpose. The approach includes an experimental design capable of providing the necessary data, as well as a procedure for preprocessing the data and extracting features that capture the information needed by the machine learning models to detect defects in the blank and the machine. To make efficient use of the small number of experiments available, the experimental design is generated using Design of Experiments methods. They consist of two parts. In the first part, a pre-selection of influencing variables relevant to the forming process is performed. In the second part of the design, the selected variables are investigated in more detail. The preprocessing procedure consists of feature engineering, feature extraction and feature selection. In the feature engineering step, the data set is augmented with time series variables that are meaningful in the domain. For feature extraction, an algorithm was developed based on the mechanisms of the r-STSF, a state-of-the-art algorithm for time series classification, extending them for multivariate time series and metric target variables. This feature extraction algorithm itself can be seen as an additional contribution of this work, because it is not tied to the application domain of monitoring flow-forming processes, but can be used as a feature extraction algorithm for multivariate time series classification in general. For feature selection, a Recursive Feature Elimination is employed. With the resulting features, random forests are trained to detect several quality features of the blank and defects of the machine. The trained models achieve good prediction accuracy for most of the target variables. This shows that the application of machine learning is a promising approach for the monitoring of flow-forming processes, which requires further investigation for confirmation. Full article

(This article belongs to the Special Issue Sensors for Machine Condition Monitoring, Diagnostics, Prognostics and Maintenance)

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28 pages, 1819 KiB

Open AccessArticle

Explainable Remaining Tool Life Prediction for Individualized Production Using Automated Machine Learning

by Lukas Krupp, Christian Wiede, Joachim Friedhoff and Anton Grabmaier

Sensors 2023, 23(20), 8523; https://doi.org/10.3390/s23208523 - 17 Oct 2023

Cited by 1 | Viewed by 945

Abstract

The increasing demand for customized products is a core driver of novel automation concepts in Industry 4.0. For the case of machining complex free-form workpieces, e.g., in die making and mold making, individualized manufacturing is already the industrial practice. The varying process conditions and demanding machining processes lead to a high relevance of machining domain experts and a low degree of manufacturing flow automation. In order to increase the degree of automation, online process monitoring and the prediction of the quality-related remaining cutting tool life is indispensable. However, the varying process conditions complicate this as the correlation between the sensor signals and tool condition is not directly apparent. Furthermore, machine learning (ML) knowledge is limited on the shop floor, preventing a manual adaption of the models to changing conditions. Therefore, this paper introduces a new method for remaining tool life prediction in individualized production using automated machine learning (AutoML). The method enables the incorporation of machining expert knowledge via the model inputs and outputs. It automatically creates end-to-end ML pipelines based on optimized ensembles of regression and forecasting models. An explainability algorithm visualizes the relevance of the model inputs for the decision making. The method is analyzed and compared to a manual state-of-the-art approach for series production in a comprehensive evaluation using a new milling dataset. The dataset represents gradual tool wear under changing workpieces and process parameters. Our AutoML method outperforms the state-of-the-art approach and the evaluation indicates that a transfer of methods designed for series production to variable process conditions is not easily possible. Overall, the new method optimizes individualized production economically and in terms of resources. Machining experts with limited ML knowledge can leverage their domain knowledge to develop, validate and adapt tool life models. Full article

(This article belongs to the Special Issue Sensors for Machine Condition Monitoring, Diagnostics, Prognostics and Maintenance)

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13 pages, 1997 KiB

Open AccessArticle

Maintenance Cost Minimization for an Agricultural Harvesting Gripper

by Florina Maria Șerdean, Mihai Dan Șerdean and Silviu-Dan Mândru

Sensors 2023, 23(8), 4103; https://doi.org/10.3390/s23084103 - 19 Apr 2023

Viewed by 1021

Abstract

A crucial aspect that has to be considered in all fields and, especially, in smart farming, a rapidly developing industry, is maintenance. Due to the costs generated by both under-maintaining and over-maintaining the components of a system, a balance has to be achieved. The paper is focused on presenting an optimal maintenance policy used to ensure cost minimization by determining the optimal time to make a preventive replacement of the actuators of a harvesting robotic system. First, a brief presentation of the gripper with Festo fluidic muscles used in a novel way instead of fingers is given. Then, the nature-inspired optimization algorithm, as well as the maintenance policy are described. The paper also includes the steps and the obtained results of the developed optimal maintenance policy applied for the Festo fluidic muscles. The outcome of the optimization shows that a significant reduction in the costs is obtained if one performs a preventive replacement of the actuators a few days before the lifetime provided by the manufacturer and the lifetime estimated using a Weibull distribution. Full article

(This article belongs to the Special Issue Sensors for Machine Condition Monitoring, Diagnostics, Prognostics and Maintenance)

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20 pages, 26208 KiB

Open AccessArticle

Leveraging Active Learning for Failure Mode Acquisition

by Amol Kulkarni, Janis Terpenny and Vittaldas Prabhu

Sensors 2023, 23(5), 2818; https://doi.org/10.3390/s23052818 - 04 Mar 2023

Cited by 2 | Viewed by 1353

Abstract

Identifying failure modes is an important task to improve the design and reliability of a product and can also serve as a key input in sensor selection for predictive maintenance. Failure mode acquisition typically relies on experts or simulations which require significant computing resources. With the recent advances in Natural Language Processing (NLP), efforts have been made to automate this process. However, it is not only time consuming, but extremely challenging to obtain maintenance records that list failure modes. Unsupervised learning methods such as topic modeling, clustering, and community detection are promising approaches for automatic processing of maintenance records to identify failure modes. However, the nascent state of NLP tools combined with incompleteness and inaccuracies of typical maintenance records pose significant technical challenges. As a step towards addressing these challenges, this paper proposes a framework in which online active learning is used to identify failure modes from maintenance records. Active learning provides a semi-supervised machine learning approach, allowing for a human in the training stage of the model. The hypothesis of this paper is that the use of a human to annotate part of the data and train a machine learning model to annotate the rest is more efficient than training unsupervised learning models. Results demonstrate that the model is trained with annotating less than ten percent of the total available data. The framework is able to achieve ninety percent (90%) accuracy in the identification of failure modes in test cases with an F-1 score of 0.89. This paper also demonstrates the effectiveness of the proposed framework with both qualitative and quantitative measures. Full article

(This article belongs to the Special Issue Sensors for Machine Condition Monitoring, Diagnostics, Prognostics and Maintenance)

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17 pages, 833 KiB

Open AccessArticle

Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier

by Prince Waqas Khan and Yung-Cheol Byun

Sensors 2022, 22(18), 6955; https://doi.org/10.3390/s22186955 - 14 Sep 2022

Cited by 14 | Viewed by 2079

Abstract

Wind turbines are widely used worldwide to generate clean, renewable energy. The biggest issue with a wind turbine is reducing failures and downtime, which lowers costs associated with operations and maintenance. Wind turbines’ consistency and timely maintenance can enhance their performance and dependability. Still, the traditional routine configuration makes detecting faults of wind turbines difficult. Supervisory control and data acquisition (SCADA) produces reliable and affordable quality data for the health condition of wind turbine operations. For wind power to be sufficiently reliable, it is crucial to retrieve useful information from SCADA successfully. This article proposes a new AdaBoost, K-nearest neighbors, and logistic regression-based stacking ensemble (AKL-SE) classifier to classify the faults of the wind turbine condition monitoring system. A stacking ensemble classifier integrates different classification models to enhance the model’s accuracy. We have used three classifiers, AdaBoost, K-nearest neighbors, and logistic regression, as base models to make output. The output of these three classifiers is used as input in the logistic regression classifier’s meta-model. To improve the data validity, SCADA data are first preprocessed by cleaning and removing any abnormal data. Next, the Pearson correlation coefficient was used to choose the input variables. The Stacking Ensemble classifier was trained using these parameters. The analysis demonstrates that the suggested method successfully identifies faults in wind turbines when applied to local 3 MW wind turbines. The proposed approach shows the potential for effective wind energy use, which could encourage the use of clean energy. Full article

(This article belongs to the Special Issue Sensors for Machine Condition Monitoring, Diagnostics, Prognostics and Maintenance)

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