Special Issue "Fault Detection, Diagnosis, and Recovery: Concept, Modeling, and Optimization"

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (30 September 2018)

Special Issue Editor

Guest Editor
Dr. Kouroush Jenab

Department of Engineering and Technology Management, Morehead State University, 150 University Blvd, Morehead, KY 40351, USA
Website | E-Mail
Interests: intelligent fault detection and recovery; condition-based monitoring; reliability; manufacturing systems; robotics; VR/RL based failure analysis

Special Issue Information

Dear Colleagues,

Fault detection and diagnosis, which is a key component of many operations management automation systems, become a complicated process in complex systems (CS) and systems of systems (SoS). In such systems, automated fault detection and diagnosis depends heavily on input from sensors or derived measures of performance that must be differentiated and analyzed through big data techniques. As a result, further development in monitoring, detection, diagnosis, and recovery techniques, modeling, and optimization is a must.

Therefore, this Special Issue will bring together papers, which particularly describe recent advances in monitoring, detection, diagnosis, and recovery mechanisms with an emphasis on CS and SoS, describing the application of novel theories across all areas for improving operation management automation. Papers that include practical experimental results are particularly encouraged.

Dr. Kouroush Jenab
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 papers will be 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. Machines is an international peer-reviewed open access quarterly 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 550 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

  • Intelligent Fault Detection and Diagnosis
  • Heuristic Methods for Fault Detection and Diagnosis
  • Big Data for Fault Detection and Diagnosis
  • Fault Isolation in Systems of Systems (SoS)
  • System Monitoring
  • Condition Based Monitoring
  • Sensors
  • Measures of Performance
  • Virtual Reality

Published Papers (7 papers)

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Research

Open AccessArticle
Development of a Low-Cost Vibration Measurement System for Industrial Applications
Received: 2 October 2018 / Revised: 2 January 2019 / Accepted: 8 January 2019 / Published: 1 February 2019
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Abstract
Vibration-Based Condition Monitoring (VBCM) provides essential data to perform Condition-Based Maintenance for efficient, optimal, reliable, and safe industrial machinery operation. However, equipment required to perform VBCM is often relatively expensive. In this paper, a low-cost vibration measurement system based on a microcontroller platform [...] Read more.
Vibration-Based Condition Monitoring (VBCM) provides essential data to perform Condition-Based Maintenance for efficient, optimal, reliable, and safe industrial machinery operation. However, equipment required to perform VBCM is often relatively expensive. In this paper, a low-cost vibration measurement system based on a microcontroller platform is presented. The FRDM K64F development board was selected as the most suitable for fulfilling the system requirements. The industrial environment is highly contaminated by noise (electromagnetic, combustion, airborne, sound borne, and mechanical noise). Developing a proper antialiasing filter to reduce industrial noise is a real challenge. In order to validate the developed system, evaluations of frequency response and phase noise were carried out. Additionally, vibration measurements were recorded in the industry under different running conditions and machine configurations. Data were collected simultaneously using a standard reference system and the low-cost vibration measurement system. Results were processed using Fast Fourier Transform and Welch’s method. Finally, a low-cost vibration measurement system was successfully created. The validation process demonstrates the robustness, reliability, and accuracy of this research approach. Results confirm a correlation between signal frequency spectrum obtained using both measurement systems. We also introduce new guidelines for practical data storage, communications, and validation process for vibration measurements. Full article
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Open AccessFeature PaperArticle
Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks
Received: 23 October 2018 / Revised: 14 November 2018 / Accepted: 15 November 2018 / Published: 20 November 2018
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Abstract
The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount [...] Read more.
The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines. Full article
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Open AccessArticle
A Reliability-Centered Maintenance Study for an Individual Section-Forming Machine
Received: 7 September 2018 / Revised: 11 October 2018 / Accepted: 23 October 2018 / Published: 26 October 2018
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Abstract
This study investigated the breakdown trend in an automated production with an aim to recommend the application of reliability-centered maintenance (RCM) for improved productivity via a new preventive maintenance (PM) program. An individual section-forming machine (ISM)—a glass blowing machine for making glass bottles—was [...] Read more.
This study investigated the breakdown trend in an automated production with an aim to recommend the application of reliability-centered maintenance (RCM) for improved productivity via a new preventive maintenance (PM) program. An individual section-forming machine (ISM)—a glass blowing machine for making glass bottles—was used as the case study for an automated production system. The machine parts and the working mechanisms were analysed with a special focus on methods of processes and procedures. This will enable the ISM maintenance department to run more effectively and achieve its essential goal of ensuring effective machine operation and reduction in machine downtime. In this work, information is provided on the steps and procedures to identify critical components of the ISM using failure modes and effect analysis (FMEA) as a tool to come up with an optimal and efficient maintenance program using the reliability data of the equipment’s functional components. A relationship between the failure rate of the machine components and the maintenance costs was established such that using the recommended PM program demonstrates evidence of an improvement in the machine’s availability, safety, and cost-effectiveness and will result in an increase in the company’s profit margin. Full article
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Open AccessFeature PaperArticle
Experimental Evidence of the Speed Variation Effect on SVM Accuracy for Diagnostics of Ball Bearings
Received: 15 September 2018 / Revised: 15 October 2018 / Accepted: 17 October 2018 / Published: 18 October 2018
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Abstract
In recent years, we have witnessed a considerable increase in scientific papers concerning the condition monitoring of mechanical components by means of machine learning. These techniques are oriented towards the diagnostics of mechanical components. In the same years, the interest of the scientific [...] Read more.
In recent years, we have witnessed a considerable increase in scientific papers concerning the condition monitoring of mechanical components by means of machine learning. These techniques are oriented towards the diagnostics of mechanical components. In the same years, the interest of the scientific community in machine diagnostics has moved to the condition monitoring of machinery in non-stationary conditions (i.e., machines working with variable speed profiles or variable loads). Non-stationarity implies more complex signal processing techniques, and a natural consequence is the use of machine learning techniques for data analysis in non-stationary applications. Several papers have studied the machine learning system, but they focus on specific machine learning systems and the selection of the best input array. No paper has considered the dynamics of the system, that is, the influence of how much the speed profile changes during the training and testing steps of a machine learning technique. The aim of this paper is to show the importance of considering the dynamic conditions, taking the condition monitoring of ball bearings in variable speed applications as an example. A commercial support vector machine tool is used, tuning it in constant speed applications and testing it in variable speed conditions. The results show critical issues of machine learning techniques in non-stationary conditions. Full article
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Open AccessArticle
Gas Path Fault and Degradation Modelling in Twin-Shaft Gas Turbines
Received: 18 August 2018 / Revised: 19 September 2018 / Accepted: 20 September 2018 / Published: 1 October 2018
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Abstract
In this study, an assessment of degradation and failure modes in the gas-path components of twin-shaft industrial gas turbines (IGTs) has been carried out through a model-based analysis. Measurements from twin-shaft IGTs operated in the field and denoting reduction in engine performance attributed [...] Read more.
In this study, an assessment of degradation and failure modes in the gas-path components of twin-shaft industrial gas turbines (IGTs) has been carried out through a model-based analysis. Measurements from twin-shaft IGTs operated in the field and denoting reduction in engine performance attributed to compressor fouling conditions, hot-end blade turbine damage, and failure in the variable stator guide vane (VSGV) mechanism of the compressor have been considered for the analysis. The measurements were compared with simulated data from a thermodynamic model constructed in a Simulink environment, which predicts the physical parameters (pressure and temperature) across the different stations of the IGT. The model predicts engine health parameters, e.g., component efficiencies and flow capacities, which are not available in the engine field data. The results show that it is possible to simulate the change in physical parameters across the IGT during degradation and failure in the components by varying component efficiencies and flow capacities during IGT simulation. The results also demonstrate that the model can predict the measured field data attributed to failure in the gas-path components of twin-shaft IGTs. The estimated health parameters during degradation or failure in the gas-path components can assist the development of health-index prognostic methods for operational engine performance prediction. Full article
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Open AccessFeature PaperArticle
Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components
Received: 15 June 2018 / Revised: 18 July 2018 / Accepted: 22 July 2018 / Published: 1 August 2018
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Abstract
This work presents a method to improve the diagnostic performance of empirical classification system (ECS), which is used to estimate the degradation state of components based on measured signals. The ECS is embedded in a homogenous continuous-time, finite-state semi-Markov model (HCTFSSMM), which adjusts [...] Read more.
This work presents a method to improve the diagnostic performance of empirical classification system (ECS), which is used to estimate the degradation state of components based on measured signals. The ECS is embedded in a homogenous continuous-time, finite-state semi-Markov model (HCTFSSMM), which adjusts diagnoses based on the past history of components. The combination gives rise to a homogeneous continuous-time finite-state hidden semi-Markov model (HCTFSHSMM). In an application involving the degradation of bearings in automotive machines, the proposed method is shown to be superior in classification performance compared to the single-stage ECS. Full article
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Open AccessArticle
Non-Destructive Testing for Winding Insulation Diagnosis Using Inter-Turn Transient Voltage Signature Analysis
Received: 4 March 2018 / Revised: 12 April 2018 / Accepted: 4 May 2018 / Published: 10 May 2018
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Abstract
The paper proposes a novel approach to assess the integrity of Electrical Insulation Systems (EIS) by evaluating the response of the Transient Voltage Signature Analysis (VSA) to voltage source inverters correlated with changes in the Insulation Capacitance (IC). The involved model structures are [...] Read more.
The paper proposes a novel approach to assess the integrity of Electrical Insulation Systems (EIS) by evaluating the response of the Transient Voltage Signature Analysis (VSA) to voltage source inverters correlated with changes in the Insulation Capacitance (IC). The involved model structures are derived from the in-situ estimation of high-frequency electromagnetic RLMC lumped network parameters. Different physical phenomena such as inductive and capacitive effects, as well as skin and proximity effects are combined. To account for these phenomena, we use an approach based on equivalent multi-transmission line electric circuits with distributed parameters (R: resistances, L, M: self and mutual inductances, and C: capacitances) which are frequency-dependent. Using the finite element method, firstly the turn-to-ground and turn-to-turn capacitance parameters are performed by solving an electrostatic model with a floating electric potential approach, and secondly, the resistance and self/mutual inductances are computed from the strongly coupled magneto-harmonic and total current density equations, including the conduction and displacement eddy current densities. The sensitivity of the capacitances is measured according to insulation thickness, and the dielectric properties are adopted to test the degradation order scenarios of the EIS and comparing their time and frequency domains of transient voltage waveform behavior with respect to healthy assessed insulation systems. Full article
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