IMPROVING EARTH-FAULT DETECTION PERFORMANCE AND SUPPLY SECURITY OF CABLED RURAL MV-NETWORKS WITH FAULT ISOLATION

Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description

Sensors ◽

10.3390/s20164599 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4599 ◽

Cited By ~ 1

Author(s):

Xiaogang Deng ◽

Zheng Zhang

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Chemical Process ◽

Detection Performance ◽

Fault Isolation ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Deep Support

As one classical anomaly detection technology, support vector data description (SVDD) has been successfully applied to nonlinear chemical process monitoring. However, the basic SVDD model cannot achieve a satisfactory fault detection performance in the complicated cases because of its intrinsic shallow learning structure. Motivated by the deep learning theory, one improved SVDD method, called ensemble deep SVDD (EDeSVDD), is proposed in order to monitor the process faults more effectively. In the proposed method, a deep support vector data description (DeSVDD) framework is firstly constructed by introducing the deep feature extraction procedure. Different to the traditional SVDD with only one feature extraction layer, DeSVDD is designed with multi-layer feature extraction structure and optimized by minimizing the data-enclosing hypersphere with the regularization of the deep network weights. Further considering the problem that DeSVDD monitoring performance is easily affected by the model structure and the initial weight parameters, an ensemble DeSVDD (EDeSVDD) is presented by applying the ensemble learning strategy based on Bayesian inference. A series of DeSVDD sub-models are generated at the parameter level and the structure level, respectively. These two levels of sub-models are integrated for a holistic monitoring model. To identify the cause variables for the detected faults, a fault isolation scheme is designed by applying the distance correlation coefficients to measure the nonlinear dependency between the original variables and the holistic monitoring index. The applications to the Tennessee Eastman process demonstrate that the proposed EDeSVDD model outperforms the traditional SVDD model and the DeSVDD model in terms of fault detection performance and can identify the fault cause variables effectively.

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Gap metric techniques and their application to fault detection performance analysis and fault isolation schemes

Automatica ◽

10.1016/j.automatica.2020.109029 ◽

2020 ◽

Vol 118 ◽

pp. 109029

Author(s):

Linlin Li ◽

Steven X. Ding

Keyword(s):

Performance Analysis ◽

Fault Detection ◽

Detection Performance ◽

Fault Isolation ◽

Gap Metric

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Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽

10.32964/tj13.1.33 ◽

2014 ◽

Vol 13 (1) ◽

pp. 33-41

Author(s):

YVON THARRAULT ◽

MOULOUD AMAZOUZ

Keyword(s):

Fault Detection ◽

Early Detection ◽

False Alarm ◽

False Alarm Rate ◽

Principal Component ◽

Fault Isolation ◽

Fault Detection And Isolation ◽

Detection Scheme ◽

Multiple Faults ◽

Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

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Image-Based Versus Signal-Based Sensors for Machine Fault Detection and Isolation

Volume 3: Engineering Systems; Heat Transfer and Thermal Engineering; Materials and Tribology; Mechatronics; Robotics ◽

10.1115/esda2014-20102 ◽

2014 ◽

Author(s):

Heshan Fernando ◽

Vedang Chauhan ◽

Brian Surgenor

Keyword(s):

Fault Detection ◽

High Speed ◽

Fault Isolation ◽

Video Data ◽

Fault Detection And Isolation ◽

Single Camera ◽

Multiple Faults ◽

Multiple Sensors ◽

Machine Fault ◽

And Performance

This paper presents the results of a comparative study that investigated the use of image-based and signal-based sensors for fault detection and fault isolation of visually-cued faults on an automated assembly machine. The machine assembles 8 mm circular parts, from a bulk-supply, onto continuously moving carriers at a rate of over 100 assemblies per minute. Common faults on the machine include part jams and ejected parts that occur at different locations on the machine. Two sensor systems are installed on the machine for detecting and isolating these faults: an image-based system consisting of a single camera and a signal-based sensor system consisting of multiple greyscale sensors and limit switches. The requirements and performance of both systems are compared for detecting six faults on the assembly machine. It is found that both methods are able to effectively detect the faults but they differ greatly in terms of cost, ease of implementation, detection time and fault isolation capability. The conventional signal-based sensors are low in cost, simple to implement and require little computing power, but the installation is intrusive to the machine and readings from multiple sensors are required for faster fault detection and isolation. The more sophisticated image-based system requires an expensive, high-resolution, high-speed camera and significantly more processing power to detect the same faults; however, the system is not intrusive to the machine, fault isolation becomes a simpler problem with video data, and the single camera is able to detect multiple faults in its field of view.

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Fault detection performance of multi-mode transmission reflection analysis for few-mode fiber link

Optics Letters ◽

10.1364/ol.446849 ◽

2021 ◽

Author(s):

Wenping Zhang ◽

Feng Liu ◽

Zhenxing He ◽

Lixin Xu ◽

Guijun Hu

Keyword(s):

Fault Detection ◽

Detection Performance ◽

Fiber Link ◽

Multi Mode

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Estimation and Fault Diagnosis of Lithium-Ion Batteries: A Fractional-Order System Approach

Mathematical Problems in Engineering ◽

10.1155/2018/8705363 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Shulan Kong ◽

Mehrdad Saif ◽

Guozeng Cui

Keyword(s):

Fault Diagnosis ◽

Fault Detection ◽

Fractional Order ◽

Sliding Mode ◽

Estimation Error ◽

Lithium Ion ◽

Fault Isolation ◽

Sliding Mode Observer ◽

Fault Detection And Isolation ◽

Order System

This study investigates estimation and fault diagnosis of fractional-order Lithium-ion battery system. Two simple and common types of observers are designed to address the design of fault diagnosis and estimation for the fractional-order systems. Fractional-order Luenberger observers are employed to generate residuals which are then used to investigate the feasibility of model based fault detection and isolation. Once a fault is detected and isolated, a fractional-order sliding mode observer is constructed to provide an estimate of the isolated fault. The paper presents some theoretical results for designing stable observers and fault estimators. In particular, the notion of stability in the sense of Mittag-Leffler is first introduced to discuss the state estimation error dynamics. Overall, the design of the Luenberger observer as well as the sliding mode observer can accomplish fault detection, fault isolation, and estimation. The effectiveness of the proposed strategy on a three-cell battery string system is demonstrated.

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Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process

Processes ◽

10.3390/pr10010122 ◽

2022 ◽

Vol 10 (1) ◽

pp. 122

Author(s):

Yang Li ◽

Fangyuan Ma ◽

Cheng Ji ◽

Jingde Wang ◽

Wei Sun

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Chemical Process ◽

Detection Method ◽

Detection Performance ◽

Detection Methods ◽

Normal Operation ◽

Discriminative Feature ◽

Inherent Feature ◽

Global And Local

Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.

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