A Comprehensive Health Indicator Integrated by the Dynamic Risk Profile from Condition Monitoring Data and the Function of Financial Losses

Large rotating machinery, such as centrifugal gas compressors and pumps, have been widely applied and acted as crucial components in the oil and gas industries. Breakdowns or deteriorated performance of these rotating machines can bring significant economic loss to the companies. In order to conduct effective maintenance and avoid unplanned downtime, a system-wide health indicator is proposed in this paper. The health indicator not only uses a dynamic risk profile, but also considers financial loss and the fault probability based on condition monitoring data. This methodology is carried out by four steps: fault detection, probability of fault calculation, consequence of fault calculation and dynamic risk assessment. In our methodology, the fault probability is calculated by robust Mahalanobis distance, presenting as a system-wide feature from a sparse autoencoder fault detection model enabled early fault detection. The value of the health indicator is presented in financial loss, which assists in effective operational decision-making in a process system. To evaluate the performance of the proposed indicator, two case studies were carried out—one case tested on multivariate industrial data obtained from a pump, and another one tested on an industrial data set from a compressor. Results prove that the integrated health indicator can detect the faults at their incipient stages, indicate the degradation of the system with dynamically updated process risk at each sampling instant, and suggest an appropriate shutdown time before the system suffers severe damage. In addition, this methodology can be adapted to other machines’ health assessments, such as those of turbines and motors. The presented method of processing the industrial data set can benefit relevant readers.

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Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

Shock and Vibration ◽

10.1155/2018/5794513 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16

Author(s):

Bo She ◽

Fuqing Tian ◽

Weige Liang ◽

Gang Zhang

Keyword(s):

Fault Detection ◽

Dimension Reduction ◽

Condition Monitoring ◽

Data Structures ◽

Principal Component ◽

Feature Space ◽

Kernel Principal Component Analysis ◽

Local Data ◽

Data Set ◽

Global And Local

The dimension reduction methods have been proved powerful and practical to extract latent features in the signal for process monitoring. A linear dimension reduction method called nonlocal orthogonal preserving embedding (NLOPE) and its nonlinear form named nonlocal kernel orthogonal preserving embedding (NLKOPE) are proposed and applied for condition monitoring and fault detection. Different from kernel orthogonal neighborhood preserving embedding (KONPE) and kernel principal component analysis (KPCA), the NLOPE and NLKOPE models aim at preserving global and local data structures simultaneously by constructing a dual-objective optimization function. In order to adjust the trade-off between global and local data structures, a weighted parameter is introduced to balance the objective function. Compared with KONPE and KPCA, NLKOPE combines both the advantages of KONPE and KPCA, and NLKOPE is also more powerful in extracting potential useful features in nonlinear data set than NLOPE. For the purpose of condition monitoring and fault detection, monitoring statistics are constructed in feature space. Finally, three case studies on the gearbox and bearing test rig are carried out to demonstrate the effectiveness of the proposed nonlinear fault detection method.

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Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals

Applied Sciences ◽

10.3390/app11052166 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2166

Author(s):

Van Bui ◽

Tung Lam Pham ◽

Huy Nguyen ◽

Yeong Min Jang

Keyword(s):

Fault Detection ◽

Data Augmentation ◽

Model Performance ◽

Original Data ◽

Fault Classification ◽

Training Process ◽

Generative Adversarial Network ◽

Data Set ◽

Adversarial Network ◽

Machine Fault

In the last decade, predictive maintenance has attracted a lot of attention in industrial factories because of its wide use of the Internet of Things and artificial intelligence algorithms for data management. However, in the early phases where the abnormal and faulty machines rarely appeared in factories, there were limited sets of machine fault samples. With limited fault samples, it is difficult to perform a training process for fault classification due to the imbalance of input data. Therefore, data augmentation was required to increase the accuracy of the learning model. However, there were limited methods to generate and evaluate the data applied for data analysis. In this paper, we introduce a method of using the generative adversarial network as the fault signal augmentation method to enrich the dataset. The enhanced data set could increase the accuracy of the machine fault detection model in the training process. We also performed fault detection using a variety of preprocessing approaches and classified the models to evaluate the similarities between the generated data and authentic data. The generated fault data has high similarity with the original data and it significantly improves the accuracy of the model. The accuracy of fault machine detection reaches 99.41% with 20% original fault machine data set and 93.1% with 0% original fault machine data set (only use generate data only). Based on this, we concluded that the generated data could be used to mix with original data and improve the model performance.

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The Psychological and Financial Impact of Cybercrime Victimization: A Novel Application of the Shattered Assumptions Theory

Social Science Computer Review ◽

10.1177/0894439320983828 ◽

2021 ◽

pp. 089443932098382

Author(s):

Jildau Borwell ◽

Jurjen Jansen ◽

Wouter Stol

Keyword(s):

Negative Impact ◽

Secondary Analysis ◽

Well Being ◽

Policy Implications ◽

Financial Impact ◽

Financial Loss ◽

Data Set ◽

Loss Compensation ◽

Shattered Assumptions ◽

The Impact

While criminality is digitizing, a theory-based understanding of the impact of cybercrime on victims is lacking. Therefore, this study addresses the psychological and financial impact of cybercrime on victims, applying the shattered assumptions theory (SAT) to predict that impact. A secondary analysis was performed on a representative data set of Dutch citizens ( N = 33,702), exploring the psychological and financial impact for different groups of cybercrime victims. The results showed a higher negative impact on emotional well-being for victims of person-centered cybercrime, victims for whom the offender was an acquaintance, and victims whose financial loss was not compensated and a lower negative impact on emotional well-being for victims with a higher income. The study led to novel scientific insights and showed the applicability of the SAT for developing hypotheses about cybercrime victimization impact. In this study, most hypotheses had to be rejected, leading to the conclusion that more work has to be done to test the applicability of the SAT in the field of cybercrime. Furthermore, policy implications were identified considering the prioritization of and approach to specific cybercrimes, treatment of victims, and financial loss compensation.

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Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data

Renewable Energy ◽

10.1016/j.renene.2017.06.089 ◽

2018 ◽

Vol 116 ◽

pp. 107-122 ◽

Cited By ~ 65

Author(s):

Phong B. Dao ◽

Wieslaw J. Staszewski ◽

Tomasz Barszcz ◽

Tadeusz Uhl

Keyword(s):

Fault Detection ◽

Condition Monitoring ◽

Wind Turbines ◽

Cointegration Analysis

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Fault severity detection of a worm gearbox based on several feature extraction methods through a developed condition monitoring system

SN Applied Sciences ◽

10.1007/s42452-020-04131-w ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Berkan Hızarcı ◽

Rafet Can Ümütlü ◽

Zeki Kıral ◽

Hasan Öztürk

Keyword(s):

Fault Detection ◽

Condition Monitoring ◽

Extraction Methods ◽

Poincaré Plot ◽

Vibration Data ◽

Sliding Motion ◽

Fault Severity ◽

Fault Features ◽

Gearbox Vibration ◽

Condition Monitoring System

AbstractThis study presents the severity detection of pitting faults on worm gearbox through the assessment of fault features extracted from the gearbox vibration data. Fault severity assessment on worm gearbox is conducted by the developed condition monitoring instrument with observing not only traditional but also multidisciplinary features. It is well known that the sliding motion between the worm gear and wheel gear causes difficulties about fault detection on worm gearboxes. Therefore, continuous monitoring and observation of different types of fault features are very important, especially for worm gearboxes. Therefore, in this study, time-domain statistics, the features of evaluated vibration analysis method and Poincaré plot are examined for fault severity detection on worm gearbox. The most reliable features for fault detection on worm gearbox are determined via the parallel coordinate plot. The abnormality detection during worm gearbox operation with the developed system is performed successfully by means of a decision tree.

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