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Special Issue "Sensors for Prognostics and Health Management"

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

Deadline for manuscript submissions: closed (31 December 2018)

Special Issue Editor

Guest Editor
Dr. Jie Liu

Dept. of Mechanical & Aerospace Engineering, Carleton University, Ottawa, ON, Canada
Website | E-Mail
Interests: sensors; prognostics and health management; smart sensing; intelligent control; battery management system

Special Issue Information

Dear Colleagues,

Prognostics and Health Management (PHM) methodologies can provide effective means for a reduction in the costs associated with the maintenance and sustainability of complex systems, equipment, and facilities through the accurate assessment of incipient damages and the reliable prediction of the remaining useful life at the component and system levels, thereby enabling predictive maintenance while replacing periodic/routine maintenance scheme. As a relatively new engineering discipline, PHM is receiving fast-growing attention and interest from both academia and industry nowadays, and has found widespread applications in aerospace, energy, manufacturing, defense, automotive, transportation, communication, and healthcare. Sensors are essential components of a typical PHM system. Effective PHM relies on advanced sensors and sensing technologies for providing informative data to estimate the health condition of the system. The MDPI journal, Sensors, is soliciting high-quality papers that document original and significant research works in “Sensors for PHM”. We welcome your participation and look forward to your contribution to this Special Issue.

Dr. Jie Liu
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. 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 1800 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

  • Sensors
  • Sensing Technology
  • Sensor Design
  • Sensor System
  • Smart Sensing
  • Remote Sensing
  • Wireless Sensing
  • Sensor Development
  • Sensor Calibration
  • Multisensory Integration
  • Self-Sensing Technology
  • Sensor Deployment
  • Sensor Data Acquisition
  • Sensor Data Analysis and Validation

Published Papers (19 papers)

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Research

Open AccessArticle Segmentation of Online Ferrograph Images with Strong Interference Based on Uniform Discrete Curvelet Transformation
Sensors 2019, 19(7), 1546; https://doi.org/10.3390/s19071546
Received: 4 March 2019 / Revised: 23 March 2019 / Accepted: 26 March 2019 / Published: 30 March 2019
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Abstract
Through real-time acquisition of the visual characteristics of wear debris in lube oil, an on-line visual ferrograph (OLVF) achieves online monitoring of equipment wear in practice. However, since a large number of bubbles can exist in lube oil and appear as a dynamically [...] Read more.
Through real-time acquisition of the visual characteristics of wear debris in lube oil, an on-line visual ferrograph (OLVF) achieves online monitoring of equipment wear in practice. However, since a large number of bubbles can exist in lube oil and appear as a dynamically changing interference shadow in OLVF ferrograms, traditional algorithms may easily misidentify the interference shadow as wear debris, resulting in a large error in the extracted wear debris characteristic. Based on this possibility, a jam-proof uniform discrete curvelet transformation (UDCT)-based method for the binarization of wear debris images was proposed. Through multiscale analysis of the OLVF ferrograms using UDCT and nonlinear transformation of UDCT coefficients, low-frequency suppression and high-frequency denoising of wear debris images were conducted. Then, the Otsu algorithm was used to achieve binarization of wear debris images under strong interference influence. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle Lifetime Estimation for Multi-Phase Deteriorating Process with Random Abrupt Jumps
Sensors 2019, 19(6), 1472; https://doi.org/10.3390/s19061472
Received: 25 December 2018 / Revised: 11 March 2019 / Accepted: 12 March 2019 / Published: 26 March 2019
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Abstract
Owing to operating condition changing, physical mutation, and sudden shocks, degradation trajectories usually exhibit multi-phase features, and the abrupt jump often appears at the changing time, which makes the traditional methods of lifetime estimation unavailable. In this paper, we mainly focus on how [...] Read more.
Owing to operating condition changing, physical mutation, and sudden shocks, degradation trajectories usually exhibit multi-phase features, and the abrupt jump often appears at the changing time, which makes the traditional methods of lifetime estimation unavailable. In this paper, we mainly focus on how to estimate the lifetime of the multi-phase degradation process with abrupt jumps at the change points under the concept of the first passage time (FPT). Firstly, a multi-phase degradation model with jumps based on the Wiener process is formulated to describe the multi-phase degradation pattern. Then, we attain the lifetime’s closed-form expression for the two-phase model with fixed jump relying on the distribution of the degradation state at the change point. Furthermore, we continue to investigate the lifetime estimation of the degradation process with random effect caused by unit-to-unit variability and the multi-phase degradation process. We extend the results of the two-phase case with fixed parameters to these two cases. For better implementation, a model identification method with off-line and on-line parts based on Expectation Maximization (EM) algorithm and Bayesian rule is proposed. Finally, a numerical case study and a practical example of gyro are provided for illustration. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE
Sensors 2019, 19(5), 1041; https://doi.org/10.3390/s19051041
Received: 26 December 2018 / Revised: 22 February 2019 / Accepted: 24 February 2019 / Published: 28 February 2019
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Abstract
The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three [...] Read more.
The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three issues, including separating the useful frequency bands from overlapped signals, extracting fault features with strong subjectivity, and processing the massive data with limited learning abilities. To address the above issues, this paper, which is based on the idea of deep learning, proposed an intelligent fault diagnosis method combining Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed based on the cross-correlation criterion. The virtual noise channel is constructed to reduce the noise of the vibration signal. Then, the de-noised signal is input into the trained SDAE model to learn the fault features adaptively. Finally, the conditions of the reciprocating compressor valve are classified by the proposed method. The results show that classification accuracy is 92.72% under the condition of a low signal-noise ratio, which is 5 percentage points higher than that of the traditional methods. This shows the effectiveness and robustness of the proposed method. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle A Parametric Design Method for Optimal Quick Diagnostic Software
Sensors 2019, 19(4), 910; https://doi.org/10.3390/s19040910
Received: 17 December 2018 / Revised: 23 January 2019 / Accepted: 11 February 2019 / Published: 21 February 2019
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Abstract
Fault diagnostic software is required to respond to faults as early as possible in time-critical applications. However, the existing methods based on early diagnosis are not adequate. First, there is no common standard to quantify the response time of a fault diagnostic software [...] Read more.
Fault diagnostic software is required to respond to faults as early as possible in time-critical applications. However, the existing methods based on early diagnosis are not adequate. First, there is no common standard to quantify the response time of a fault diagnostic software to the fault. Second, none of these methods take into account how the objective to improve the response time may affect the accuracy of the designed fault diagnostic software. In this work, a measure of the response time is provided, which was formulated using the time complexity of the algorithm and the signal acquisition time. Model optimization was built into the designed method. Its objective was to minimize the response time. The constraint of the method is to guarantee diagnostic accuracy to no less than the required accuracy. An improved feature selection method was used to solve the optimization modeling. After that, the design parameter of the optimal quick diagnostic software was obtained. Finally, the parametric design method was evaluated with two sets of experiments based on real-world bearing vibration data. The results demonstrated that optimal quick diagnostic software with a pre-defined accuracy could be obtained through the parametric design method. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle Bio-Inspired PHM Model for Diagnostics of Faults in Power Transformers Using Dissolved Gas-in-Oil Data
Sensors 2019, 19(4), 845; https://doi.org/10.3390/s19040845
Received: 30 December 2018 / Revised: 12 February 2019 / Accepted: 13 February 2019 / Published: 18 February 2019
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Abstract
Prognostics and Health Management (PHM) is an emerging technique which can improve the availability and efficiency of equipment. A series of related optimization of the PHM system has been achieved due to the growing need for lowering the cost of maintenance. The PHM [...] Read more.
Prognostics and Health Management (PHM) is an emerging technique which can improve the availability and efficiency of equipment. A series of related optimization of the PHM system has been achieved due to the growing need for lowering the cost of maintenance. The PHM system highly relies on data collected from its components. Based on the theory of machine learning, this paper proposes a bio-inspired PHM model based on a dissolved gas-in-oil dataset (DGA) to diagnose faults of transformes in power grids. Specifically, this model applies Bat algorithm (BA), a metaheuristic population-based algorithm, to optimize the structure of the Back-propagation neural network (BPNN). Furthermore, this paper proposes a modified Bat algorithm (MBA); here the chaos strategy is utilized to improve the random initialization process of BA in order to avoid falling into local optima. To prove that the proposed PHM model has better fault diagnostic performance than others, fitness and mean squared error (MSE) of Bat-BPNN are set as reference amounts to compare with other power grid PHM approaches including BPNN, Particle swarm optimization (PSO)-BPNN, as well as Genetic algorithm (GA)-BPNN. The experimental results show that the BA-BPNN model has increased the fault diagnosis accuracy from 77.14% to 97.14%, which is higher than other power transformer PHM models. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle Segmentation of the ECG Signal by Means of a Linear Regression Algorithm
Sensors 2019, 19(4), 775; https://doi.org/10.3390/s19040775
Received: 15 December 2018 / Revised: 1 February 2019 / Accepted: 6 February 2019 / Published: 14 February 2019
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Abstract
The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to [...] Read more.
The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle UAV Sensor Fault Detection Using a Classifier without Negative Samples: A Local Density Regulated Optimization Algorithm
Sensors 2019, 19(4), 771; https://doi.org/10.3390/s19040771
Received: 31 December 2018 / Revised: 28 January 2019 / Accepted: 31 January 2019 / Published: 13 February 2019
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Abstract
Fault detection for sensors of unmanned aerial vehicles is essential for ensuring flight security, in which the flight control system conducts real-time control for the vehicles relying on the sensing information from sensors, and erroneous sensor data will lead to false flight control [...] Read more.
Fault detection for sensors of unmanned aerial vehicles is essential for ensuring flight security, in which the flight control system conducts real-time control for the vehicles relying on the sensing information from sensors, and erroneous sensor data will lead to false flight control commands, causing undesirable consequences. However, because of the scarcity of faulty instances, it still remains a challenging issue for flight sensor fault detection. The one-class support vector machine approach is a favorable classifier without negative samples, however, it is sensitive to outliers that deviate from the center and lacks a mechanism for coping with them. The compactness of its decision boundary is influenced, leading to the degradation of detection rate. To deal with this issue, an optimized one-class support vector machine approach regulated by local density is proposed in this paper, which regulates the tolerance extents of its decision boundary to the outliers according to their extent of abnormality indicated by their local densities. The application scope of the local density theory is narrowed to keep the internal instances unchanged and a rule for assigning the outliers continuous density coefficients is raised. Simulation results on a real flight control system model have proved its effectiveness and superiority. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle 3D SSY Estimate of EPFM Constraint Parameter under Biaxial Loading for Sensor Structure Design
Sensors 2019, 19(3), 735; https://doi.org/10.3390/s19030735
Received: 3 January 2019 / Revised: 5 February 2019 / Accepted: 9 February 2019 / Published: 12 February 2019
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Abstract
Conventional sensor structure design and related fracture mechanics analysis are based on the single J-integral parameter approach of elastic-plastic fracture mechanics (EPFM). Under low crack constraint cases, the EPFM one-parameter approach generally gives a stress overestimate, which results in a great cost [...] Read more.
Conventional sensor structure design and related fracture mechanics analysis are based on the single J-integral parameter approach of elastic-plastic fracture mechanics (EPFM). Under low crack constraint cases, the EPFM one-parameter approach generally gives a stress overestimate, which results in a great cost waste of labor and sensor components. The J-A two-parameter approach overcomes this limitation. To enable the extensive application of the J-A approach on theoretical research and sensor engineering problem, under small scale yielding (SSY) conditions, the authors developed an estimate method to conveniently and quickly obtain the constraint (second) parameter A values directly from T-stress. Practical engineering application of sensor structure analysis and design focuses on three-dimensional (3D) structures with biaxial external loading, while the estimate method was developed based on two-dimensional (2D) plain strain condition with uniaxial loading. In the current work, the estimate method was successfully extended to a 3D structure with biaxial loading cases, which is appropriate for practical sensor design. The estimate method extension and validation process was implemented through a thin 3D single edge cracked plate (SECP) specimen. The process implementation was completed in two specified planes of 3D SECP along model thickness. A wide range of material and geometrical properties were applied for the extension and validation process, with material hardening exponent value 3, 5 and 10, and crack length ratio 0.1, 0.3 and 0.7. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle A Wear Debris Segmentation Method for Direct Reflection Online Visual Ferrography
Sensors 2019, 19(3), 723; https://doi.org/10.3390/s19030723
Received: 30 December 2018 / Revised: 4 February 2019 / Accepted: 6 February 2019 / Published: 11 February 2019
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Abstract
Wear debris in lube oil was observed using a direct reflection online visual ferrograph (OLVF) to monitor the machine running condition and judge wear failure online. The existing research has mainly concentrated on extraction of wear debris concentration and size according to ferrograms [...] Read more.
Wear debris in lube oil was observed using a direct reflection online visual ferrograph (OLVF) to monitor the machine running condition and judge wear failure online. The existing research has mainly concentrated on extraction of wear debris concentration and size according to ferrograms under transmitted light. Reports on the segmentation algorithm of the wear debris ferrograms under reflected light are lacking. In this paper, a wear debris segmentation algorithm based on edge detection and contour classification is proposed. The optimal segmentation threshold is obtained by an adaptive canny algorithm, and the contour classification filling method is applied to overcome the problems of excessive brightness or darkness of some wear debris that is often neglected by traditional segmentation algorithms such as the Otsu and Kittler algorithms. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle Collective Anomalies Detection for Sensing Series of Spacecraft Telemetry with the Fusion of Probability Prediction and Markov Chain Model
Sensors 2019, 19(3), 722; https://doi.org/10.3390/s19030722
Received: 31 December 2018 / Revised: 24 January 2019 / Accepted: 31 January 2019 / Published: 11 February 2019
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Abstract
Telemetry series, generally acquired from sensors, are the only basis for the ground management system to judge the working performance and health status of orbiting spacecraft. In particular, anomalies within telemetry can reflect sensor failure, transmission errors, and the major faults of the [...] Read more.
Telemetry series, generally acquired from sensors, are the only basis for the ground management system to judge the working performance and health status of orbiting spacecraft. In particular, anomalies within telemetry can reflect sensor failure, transmission errors, and the major faults of the related subsystem. Therefore, anomaly detection for telemetry series has drawn great attention from the aerospace area, where probability prediction methods, e.g., Gaussian process regression and relevance vector machine, have an inherent advantage for anomaly detection in time series with uncertainty presentation. However, labelling a single point with probability prediction faces many isolated false alarms, as well as a lower detection rate for collective anomalies that significantly limits its practical application. Simple sliding window fusion can decrease the false positives, but the support number of anomalies within the sliding window is difficult to set effectively for different series. Therefore, in this work, fused with the probability prediction-based method, the Markov chain is designed to compute the support probability of each testing series to realize the improvement on collective anomaly mode. The experiments on simulated data sets and the actual telemetry series validated the effectiveness and applicability of our proposed method. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle A Simplified SSY Estimate Method to Determine EPFM Constraint Parameter for Sensor Design
Sensors 2019, 19(3), 717; https://doi.org/10.3390/s19030717
Received: 3 January 2019 / Revised: 6 February 2019 / Accepted: 7 February 2019 / Published: 10 February 2019
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Abstract
To implement a sensor structure analysis and design (as well as other engineering applications), a two-parameter approach using elastic–plastic fracture mechanics (EPFM) could be applied to analyze a structure more accurately than a one-parameter approach, especially for structures with low crack constraint. The [...] Read more.
To implement a sensor structure analysis and design (as well as other engineering applications), a two-parameter approach using elastic–plastic fracture mechanics (EPFM) could be applied to analyze a structure more accurately than a one-parameter approach, especially for structures with low crack constraint. The application of the J-A two-parameter approach on sensors and other structures depends on the obtainment of a constraint parameter A. To conveniently and effectively obtain the A parameter values, the authors have developed a T-stress-based estimate method under a small-scale yielding (SSY) condition. Under a uniaxial external loading condition, a simplified format of the T-stress-based estimate has been proposed by the authors to obtain the parameter A much more conveniently and effectively. Generally, sensors and other practical engineering structures endure biaxial external loading instead of the uniaxial one. In the current work, the simplified formation of the estimate method is extended to a biaxial loading condition. By comparing the estimated A parameter values with their numerical solutions from a finite element analysis (FEA) results, the extension of the simplified formation of T-stress-based estimate method to biaxial loading was discussed and validated. The comparison procedure was completed using a wide variety of materials and geometrical properties on three types of specimens: single edge cracked plate (SECP), center cracked plate (CCP), and double edge cracked plate (DECP). Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle Sliding Mode Fault Tolerant Control for Unmanned Aerial Vehicle with Sensor and Actuator Faults
Sensors 2019, 19(3), 643; https://doi.org/10.3390/s19030643
Received: 27 December 2018 / Revised: 23 January 2019 / Accepted: 29 January 2019 / Published: 3 February 2019
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Abstract
The unmanned aerial vehicle (UAV) has been developing rapidly recently, and the safety and the reliability of the UAV are significant to the mission execution and the life of UAV. Sensor and actuator failures of a UAV are one of the most common [...] Read more.
The unmanned aerial vehicle (UAV) has been developing rapidly recently, and the safety and the reliability of the UAV are significant to the mission execution and the life of UAV. Sensor and actuator failures of a UAV are one of the most common malfunctions, threating the safety and life of the UAV. Fault-tolerant control technology is an effective method to improve the reliability and safety of UAV, which also contributes to vehicle health management (VHM). This paper deals with the sliding mode fault-tolerant control of the UAV, considering the failures of sensor and actuator. Firstly, a terminal sliding surface is designed to ensure the state of the system on the sliding mode surface throughout the control process based on the simplified coupling dynamic model. Then, the sliding mode control (SMC) method combined with the RBF neural network algorithm is used to design the parameters of the sliding mode controller, and with this, the efficiency of the design process is improved and system chattering is minimized. Finally, the Simulink simulations are carried out using a fault tolerance controller under the conditions where accelerometer sensor, gyroscope sensor or actuator failures is assumed. The results show that the proposed control strategy is quite an effective method for the control of UAVs with accelerometer sensor, gyroscope sensor or actuator failures. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle A Mission Reliability-Driven Manufacturing System Health State Evaluation Method Based on Fusion of Operational Data
Sensors 2019, 19(3), 442; https://doi.org/10.3390/s19030442
Received: 31 December 2018 / Revised: 16 January 2019 / Accepted: 18 January 2019 / Published: 22 January 2019
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Abstract
The rapid development of complexity and intelligence in manufacturing systems leads to an increase in potential operational risks and therefore requires a more comprehensive system-level health diagnostics approach. Based on the massive multi-source operational data collected by smart sensors, this paper proposes a [...] Read more.
The rapid development of complexity and intelligence in manufacturing systems leads to an increase in potential operational risks and therefore requires a more comprehensive system-level health diagnostics approach. Based on the massive multi-source operational data collected by smart sensors, this paper proposes a mission reliability-driven manufacturing system health state evaluation method. Characteristic attributes affecting the mission reliability are monitored and analyzed based on different sensor groups, including the performance state of the manufacturing equipment, the execution state of the production task and the quality state of the manufactured product. The Dempster-Shafer (D-S) evidence theory approach is used to diagnose the health state of the manufacturing system. Results of a case study show that the proposed evaluation method can dynamically and effectively characterize the actual health state of manufacturing systems. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle Differential Equation-Based Prediction Model for Early Change Detection in Transient Running Status
Sensors 2019, 19(2), 412; https://doi.org/10.3390/s19020412
Received: 23 December 2018 / Revised: 16 January 2019 / Accepted: 18 January 2019 / Published: 20 January 2019
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Abstract
Early detection of changes in transient running status from sensor signals attracts increasing attention in modern industries. To achieve this end, this paper presents a new differential equation-based prediction model that can realize one-step-ahead prediction of machine status. Together with this model, an [...] Read more.
Early detection of changes in transient running status from sensor signals attracts increasing attention in modern industries. To achieve this end, this paper presents a new differential equation-based prediction model that can realize one-step-ahead prediction of machine status. Together with this model, an analysis of continuous monitoring of condition signal by means of a null hypothesis testing is presented to inspect/diagnose whether an abnormal status change occurs or not during successive machine operations. The detection operation is executed periodically and continuously, such that the machine running status can be monitored with an online and real-time manner. The effectiveness of the proposed method is demonstrated using three representative real-engineering applications: external loading status monitoring, bearing health status monitoring and speed condition monitoring. The method is also compared with those benchmark methods reported in the literature. From the results, the proposed method demonstrates significant improvements over others, which suggests its superiority and great potentials in real applications. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle Machine Learning-Based Sensor Data Modeling Methods for Power Transformer PHM
Sensors 2018, 18(12), 4430; https://doi.org/10.3390/s18124430
Received: 16 November 2018 / Revised: 9 December 2018 / Accepted: 13 December 2018 / Published: 14 December 2018
Cited by 1 | PDF Full-text (3474 KB) | HTML Full-text | XML Full-text
Abstract
An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. [...] Read more.
An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM models depend on the smart sensors and data generated from sensors. This paper proposed a machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid. In particular, we apply the Cuckoo Search (CS) algorithm to optimize the Back-propagation (BP) neural network in order to build high performance fault diagnostics models. The models were developed using sensor data called dissolved gas data in oil of the power transformer. We validated the models using real sensor data collected from power transformers in China. The results demonstrate that the developed meta heuristic algorithm for optimizing the parameters of the neural network is effective and useful; and machine learning-based models significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter
Sensors 2018, 18(12), 4190; https://doi.org/10.3390/s18124190
Received: 20 October 2018 / Revised: 24 November 2018 / Accepted: 26 November 2018 / Published: 29 November 2018
Cited by 2 | PDF Full-text (690 KB) | HTML Full-text | XML Full-text
Abstract
Electro-Mechanical Actuators (EMA) have attracted growing attention with their increasing incorporation in More Electric Aircraft. The performance degradation assessment of EMA needs to be studied, in which EMA motor voltage is an essential parameter, to ensure its reliability and safety of EMA. However, [...] Read more.
Electro-Mechanical Actuators (EMA) have attracted growing attention with their increasing incorporation in More Electric Aircraft. The performance degradation assessment of EMA needs to be studied, in which EMA motor voltage is an essential parameter, to ensure its reliability and safety of EMA. However, deviation exists between motor voltage monitoring data and real motor voltage due to electromagnetic interference. To reduce the deviation, EMA motor voltage estimation generally requires an accurate voltage state equation which is difficult to obtain due to the complexity of EMA. To address this problem, a Feature-aided Kalman Filter (FAKF) method is proposed, in which the state equation is substituted by a physical model of current and voltage. Consequently, voltage state data can be obtained through current monitoring data and a current–voltage model. Furthermore, voltage estimation can be implemented by utilizing voltage state data and voltage monitoring data. To validate the effectiveness of the FAKF-based estimation method, experiments have been conducted based on the published data set from NASA’s Flyable Electro-Mechanical Actuator (FLEA) test stand. The experiment results demonstrate that the proposed method has good performance in EMA motor voltage estimation. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessCommunication Pressure Monitoring Cell for Constrained Battery Electrodes
Sensors 2018, 18(11), 3808; https://doi.org/10.3390/s18113808
Received: 4 October 2018 / Revised: 31 October 2018 / Accepted: 2 November 2018 / Published: 6 November 2018
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Abstract
Testing of improved battery components and new electrochemical energy storage materials in a coin cell format as a test cell is becoming the state of the art. The pressure on the electrode surfaces inside an electrochemical cell is one of the important parameters [...] Read more.
Testing of improved battery components and new electrochemical energy storage materials in a coin cell format as a test cell is becoming the state of the art. The pressure on the electrode surfaces inside an electrochemical cell is one of the important parameters for high ionic/electronic conductivity and the cyclic lifetime. A self-designed pressure monitoring cell allows both applying an adjustable pressure and monitoring the state of charge-dependent cell pressure during cycling. The load cell shows a reciprocal behavior of the temperature sensitivity dependent on the ambient temperature and requires constant temperature conditions while monitoring the cell pressure. Further, dependent on the initial cell pressure, the relaxation time of the assembled pressure monitoring cell must be considered. The present paper describes the setup, the influence of the environment temperature and the mechanical relaxation of the pressure monitoring cell. The first cycling results, using an NCM/graphite coin cell, demonstrate the functionality of the pressure monitoring cell measuring the cell’s pressure as a function of the C-rate. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle Non-Intrusive Cable Fault Diagnosis Based on Inductive Directional Coupling
Sensors 2018, 18(11), 3724; https://doi.org/10.3390/s18113724
Received: 12 September 2018 / Revised: 15 October 2018 / Accepted: 16 October 2018 / Published: 1 November 2018
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Abstract
This paper presents and applies an inductive directional coupling technology based on spread spectrum time domain reflectometry (SSTDR) for non-intrusive power cable fault diagnosis. Different from existing capacitive coupling approaches with large signal attenuation, an inductive coupling approach with a capacitive trapper is [...] Read more.
This paper presents and applies an inductive directional coupling technology based on spread spectrum time domain reflectometry (SSTDR) for non-intrusive power cable fault diagnosis. Different from existing capacitive coupling approaches with large signal attenuation, an inductive coupling approach with a capacitive trapper is proposed to restrict the detection signal from transmitting to power source and to eliminate the effect of the power source impedance mismatch. The development, analysis, and implementation of the proposed approach are discussed in detail. A series of simulations and experiments on cables with different fault modes are conducted, along with comparison of existing capacitive coupling, to verify and demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Open AccessArticle Effect of Sensor Set Size on Polymer Electrolyte Membrane Fuel Cell Fault Diagnosis
Sensors 2018, 18(9), 2777; https://doi.org/10.3390/s18092777
Received: 26 July 2018 / Revised: 14 August 2018 / Accepted: 21 August 2018 / Published: 23 August 2018
Cited by 2 | PDF Full-text (1038 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a comparative study on the performance of different sizes of sensor sets on polymer electrolyte membrane (PEM) fuel cell fault diagnosis. The effectiveness of three sizes of sensor sets, including fuel cell voltage only, all the available sensors, and selected [...] Read more.
This paper presents a comparative study on the performance of different sizes of sensor sets on polymer electrolyte membrane (PEM) fuel cell fault diagnosis. The effectiveness of three sizes of sensor sets, including fuel cell voltage only, all the available sensors, and selected optimal sensors in detecting and isolating fuel cell faults (e.g., cell flooding and membrane dehydration) are investigated using the test data from a PEM fuel cell system. Wavelet packet transform and kernel principal component analysis are employed to reduce the dimensions of the dataset and extract features for state classification. Results demonstrate that the selected optimal sensors can provide the best diagnostic performance, where different fuel cell faults can be detected and isolated with good quality. Full article
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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