Special Issue "A Special Paper Collection from the Asia Pacific Conference of the Prognostics and Health Management (PHM) Society 2019 (PHMAP 2019)"

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (29 February 2020).

Special Issue Editors

Prof. Dr. David He
Website
Guest Editor
1. School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China
2. Department of Mechanical and Industrial Engineering (MC 251), University of Illinois at Chicago, Chicago, IL 60661, USA
Interests: Equipment health monitoring and fault diagnosis, Prognostics and health management (PHM), Failure analysis, Reliability and quality engineering, Manufacturing systems, Signal processing, Acoustic emission
Special Issues and Collections in MDPI journals
Prof. Renxiang Chen

Guest Editor
Chonqing Jiaotong University, Chongqing, China
Prof. Xiaoyang Li

Guest Editor
Beihang University, Beijing, China

Special Issue Information

Dear Colleagues,

Prognostics and health management (PHM) is considered an emerging engineering discipline that links studies of failure mechanisms to system lifecycle management. Many PHM applications can be found in but are not limited to manufacturing applications, heavy vehicle and mining application, power generation applications, aerospace and defense applications, automotive and electric vehicle applications, and railway applications. As a leading PHM conference in the Asia Pacific region, the Asia Pacific Conference of the PHM Society 2019 (PHMAP 2019) will be held in Beijing, China on July 23–25, 2019. Papers presented in PHMAP 2019 will include the following topics: the physics of failures and degradation; model-based prognostics; monitoring, diagnostic, and prognostic methods; advanced sensing technologies; data-driven prognostics; industrial big data analytics; condition-based maintenance; anomaly detection and process control; remaining useful life predictions; accelerated life and degradation tests; PHM standards, verifications, and validation; structural health management; PHM in smart manufacturing systems; PHM for electronics, components, and MEMS; PHM for automotive, marine, and heavy industry; PHM for energy and smart grid applications; PHM and reliability design for software; industrial Internet of Things and cloud computing for PHM; maintenance activities planning and scheduling optimization; hazard modeling and risk assessment. High-quality papers prsented in PHMAP 2019 will be selected to be included in this Special Issue.

Prof. David He
Prof. Xiaoyang Li
Prof. Renxiang Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • PHM
  • physics of failures and degradation
  • prognostics, diagnostics, and anomaly detection
  • industrial big data analytics
  • accelerated life and degradation tests
  • condition-based maintenance
  • structural health management
  • industrial internet of things and cloud computing
  • hazard modeling and risk assessment
  • maintenance activities planning and scheduling optimization

Published Papers (7 papers)

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Research

Open AccessArticle
Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings
Appl. Sci. 2020, 10(7), 2443; https://doi.org/10.3390/app10072443 - 03 Apr 2020
Abstract
The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, [...] Read more.
The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability. Full article
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Open AccessArticle
Transfer Learning Strategies for Deep Learning-based PHM Algorithms
Appl. Sci. 2020, 10(7), 2361; https://doi.org/10.3390/app10072361 - 30 Mar 2020
Abstract
As we enter the era of big data, we have to face big data generated by industrial systems that are massive, diverse, high-speed, and variability. In order to effectively deal with big data possessing these characteristics, deep learning technology has been widely used. [...] Read more.
As we enter the era of big data, we have to face big data generated by industrial systems that are massive, diverse, high-speed, and variability. In order to effectively deal with big data possessing these characteristics, deep learning technology has been widely used. However, the existing methods require great human involvement that is heavily depend on domain expertise and may thus be non-representative and biased from task to similar task, so for a wide variety of prognostic and health management (PHM) tasks, how to apply the developed deep learning algorithms to similar tasks to reduce the amount of development and data collection costs has become an urgent problem. Based on the idea of transfer learning and the structures of deep learning PHM algorithms, this paper proposes two transfer strategies via transferring different elements of deep learning PHM algorithms, analyzes the possible transfer scenarios in practical application, and proposes transfer strategies applicable in each scenario. At the end of this paper, the deep learning algorithm of bearing fault diagnosis based on convolutional neural networks (CNN) is transferred based on the proposed method, which was carried out under different working conditions and for different objects, respectively. The experiments verify the value and effectiveness of the proposed method and give the best choice of transfer strategy. Full article
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Open AccessArticle
Sparse Low-Rank Based Signal Analysis Method for Bearing Fault Feature Extraction
Appl. Sci. 2020, 10(7), 2358; https://doi.org/10.3390/app10072358 - 30 Mar 2020
Abstract
The condition monitoring of rolling element bearings (REBs) is essential to maintain the reliable operation of rotating machinery, and the difficulty lies in how to estimate fault information from the raw signal that is always overwhelmed by severe background noise and other interferences. [...] Read more.
The condition monitoring of rolling element bearings (REBs) is essential to maintain the reliable operation of rotating machinery, and the difficulty lies in how to estimate fault information from the raw signal that is always overwhelmed by severe background noise and other interferences. The method based on a sparse model has attracted increasing attention because it can capture deep-level fault features. However, when processing a signal with complex components and weak fault features, the performance of sparse model-based methods is often not ideal. In this work, the fault information-based sparse low-rank algorithm (FISLRA) is proposed to abstract the fault information from a noisy signal interfered with by background noise and external interference. Concretely, a sparse and low-rank model is formulated in the time-frequency domain. Then, a fast-converging algorithm is derived based on the alternating direction method of multipliers (ADMM) to solve the formulated model. Moreover, to further highlight the periodical transients, a correlated kurtosis-based thresholding (CKT) scheme proposed in this paper is also incorporated to solve the proposed low-rank spares model. The superiority of the proposed FISLRA over the traditional sparse low-rank model (TSLRM) and spectral kurtosis (SK) is proved by simulation analysis. In addition, two experimental signals collected from a bearing test rig are utilized to demonstrate the efficiency of the proposed FISLRA in fault detection. The results illustrate that compared to the TSLRM method, FISLRA can effectively extract periodical fault transients even when harmonic components (HCs) are present in the noisy signal. Full article
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Open AccessArticle
Simulation Research on Sparse Reconstruction for Defect Signals of Flip Chip Based on High-Frequency Ultrasound
Appl. Sci. 2020, 10(4), 1292; https://doi.org/10.3390/app10041292 - 14 Feb 2020
Abstract
Flip chip technology has been widely used in various fields. As the density of the solder balls in flip chip technology is increasing, the pitch among solder balls is narrowing, and the size effect is more significant. Therefore, the micro defects of the [...] Read more.
Flip chip technology has been widely used in various fields. As the density of the solder balls in flip chip technology is increasing, the pitch among solder balls is narrowing, and the size effect is more significant. Therefore, the micro defects of the solder balls are more difficult to detect. In order to ensure the reliability of the flip chip, it is very important to detect and evaluate the micro defects of solder balls. High-frequency ultrasonic testing technology is an effective micro-defect detection method. In this paper, the interaction mechanism between high-frequency ultrasonic pulse and micro defects is analyzed by finite element simulation. A transient simulation model for the whole process of ultrasonic scanning of micro defects is established to simulate scanning in acoustic microscopy imaging. The acoustic propagation path map is obtained for analyzing acoustic energy transmission during detection, and the edge blurring effect in micro-defect imaging detection is clarified. The processing method of the time-domain signal and cross-section image signal of micro defects based on sparse reconstruction is studied, which can effectively improve the accuracy of detection and the signal-to-noise ratio. Full article
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Open AccessArticle
Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network
Appl. Sci. 2020, 10(3), 770; https://doi.org/10.3390/app10030770 - 22 Jan 2020
Abstract
Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and [...] Read more.
Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods. Full article
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Open AccessArticle
A Theoretical Model with the Effect of Cracks in the Local Spalling of Full Ceramic Ball Bearings
Appl. Sci. 2019, 9(19), 4142; https://doi.org/10.3390/app9194142 - 03 Oct 2019
Abstract
For full ceramic ball bearings, cracks occur frequently in the spalling on the rings, which leads to impacts on the bearing dynamic characteristics. In this paper, the spalling is set on the outer ring, and the dynamic model considering the effect of crack [...] Read more.
For full ceramic ball bearings, cracks occur frequently in the spalling on the rings, which leads to impacts on the bearing dynamic characteristics. In this paper, the spalling is set on the outer ring, and the dynamic model considering the effect of crack is proposed. The crack is considered to be related to the strain energy, and the effect on the stiffness of the outer ring is also analyzed. Results show that the appearance of cracks leads to the reduction of the full ceramic bearing stiffness, and the vibration amplitude of bearing increases. The effect of a crack depends on its size, and the vibration of the bearing with cracks of different widths and depths vary greatly. This study provides theoretical basis for the calculation of full ceramic bearing and is of great significance for the state monitoring and fault diagnosis. Full article
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Open AccessArticle
A Cost-Effective Solution to Improving the Electrical Performance of Metal Contacting Interfaces in IC System under Temperature-Humidity Environment
Appl. Sci. 2019, 9(19), 3950; https://doi.org/10.3390/app9193950 - 20 Sep 2019
Abstract
Temperature-humidity (TH) induced failure mechanism (FM) of metal contacting interfaces in integrated circuit (IC) systems has played a significant role in system reliability issues. This paper focuses on central processing unit (CPU)/motherboard interfaces and studies several factors that are believed to have a [...] Read more.
Temperature-humidity (TH) induced failure mechanism (FM) of metal contacting interfaces in integrated circuit (IC) systems has played a significant role in system reliability issues. This paper focuses on central processing unit (CPU)/motherboard interfaces and studies several factors that are believed to have a great impact on TH performance. They include: Enabling load, surface finish quality, and contacting area. Test vehicles (TVs) of Clarkdale package and of Ibex peak motherboard were designed to measure low level contact resistance (LLCR) for catching any failure. Several sets of design of experiments (DOE) were conducted on 85°C/85% relative humidity and test results were analyzed. A proposal that correlates asperity spots and contact tip design with contact resistance was proposed and thus a cost-effective solution for improving electrical performance under TH was deduced. The proposal has proven to be reasonably effective in practice. Full article
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