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

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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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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RNN-based Fault Detection Method for MMC Photovoltaic Grid-connected System

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096514666210917150429 ◽

2021 ◽

Vol 14 ◽

Author(s):

Yuqi Pang ◽

Gang Ma ◽

Xiaotian Xu ◽

Xunyu Liu ◽

Xinyuan Zhang

Keyword(s):

Fault Detection ◽

Detection Method ◽

Circuit Breaker ◽

Detection Methods ◽

Multilevel Converters ◽

Modular Multilevel Converters ◽

Dc Circuit Breaker ◽

Linear Elements ◽

Hidden Layer ◽

Analytical Expressions

Background: Fast and reliable fault detection methods are the main technical challenges faced by photovoltaic grid-connected systems through modular multilevel converters (MMC) during the development. Objective: Existing fault detection methods have many problems, such as the inability of non-linear elements to form accurate analytical expressions, the difficulty of setting protection thresholds and the long detection time. Method: Aiming at the problems above, this paper proposes a rapid fault detection method for photovoltaic grid-connected systems based on Recurrent Neural Network (RNN). Results: The phase-to-mode transformation is used to extract the fault feature quantity to get the RNN input data. The hidden layer unit of the RNN is trained through a large amount of simulation data, and the opening instruction is given to the DC circuit breaker. Conclusion: The simulation verification results show that the proposed fault detection method has the advantage of faster detection speed without difficulties in setting and complicated calculation.

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Adjacent Infrared Multitarget Detection Using Robust Background Estimation

Journal of Sensors ◽

10.1155/2016/7279081 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Sungho Kim ◽

Kyung-Tae Kim

Keyword(s):

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Detection Method ◽

Detection Performance ◽

Detection Methods ◽

Constant False Alarm Rate ◽

Cluttered Environments ◽

Background Estimation ◽

Cfar Detection

Small target detection is very important for infrared search and track (IRST) problems. Grouped targets are difficult to detect using the conventional constant false alarm rate (CFAR) detection method. In this study, a novel multitarget detection method was developed to identify adjacent or closely spaced small infrared targets. The neighboring targets decrease the signal-to-clutter ratio in hysteresis threshold-based constant false alarm rate (H-CFAR) detection, which leads to poor detection performance in cluttered environments. The proposed adjacent target rejection-based robust background estimation can reduce the effects of the neighboring targets and enhance the small multitarget detection performance in infrared images by increasing the signal-to-clutter ratio. The experimental results of the synthetic and real adjacent target sequences showed that the proposed method produces an upgraded detection rate with the same false alarm rate compared to the recent target detection methods (H-CFAR, Top-hat, and TDLMS).

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Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

Applied Sciences ◽

10.3390/app10072443 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2443

Author(s):

Huaitao Shi ◽

Jin Guo ◽

Xiaotian Bai ◽

Lei Guo ◽

Zhenpeng Liu ◽

...

Keyword(s):

Fault Detection ◽

Nonlinear Dynamic ◽

Detection Method ◽

Normal Operation ◽

Rolling Bearings ◽

Detection Delay ◽

Detection Technology ◽

Non Linear ◽

Incipient Fault ◽

Incipient Fault Detection

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.

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Improved FAST Corner Detection Based on Harris Algorithm for Chinese Characters

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.850-851.767 ◽

2013 ◽

Vol 850-851 ◽

pp. 767-770 ◽

Cited By ~ 1

Author(s):

Na Yao ◽

Tie Cheng Bai ◽

Jie Chen

Keyword(s):

Feature Extraction ◽

Fast Algorithm ◽

Detection Rate ◽

Detection Method ◽

Corner Detection ◽

Detection Methods ◽

Chinese Characters ◽

Line Segments ◽

Running Time ◽

Shape Feature Extraction

According to the characteristics of Chinese characters image, we propose an improved corner detection method based on FAST algorithm and Harris algorithm to improve detection rate and shorten the running time for next feature extraction in this paper. The image of Chinese characters is detected for corners using FAST algorithm Firstly. Second, computing corner response function (CRF) of Harris algorithm, false corners are removed. The corners founded lastly are the endpoints of line segments, providing the length of line segments for shape feature extraction. The proposed method is compared with several corner detection methods over a number of images. Experimental results show that the proposed method shows better performance in terms of detection rate and running time.

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Analysis of the Effects of Arc Volt–Ampere Characteristics on Different Loads and Detection Methods of Series Arc Faults

Energies ◽

10.3390/en12020323 ◽

2019 ◽

Vol 12 (2) ◽

pp. 323 ◽

Cited By ~ 8

Author(s):

Qiwei Lu ◽

Zeyu Ye ◽

Yilei Zhang ◽

Tao Wang ◽

Zhixuan Gao

Keyword(s):

Fault Detection ◽

Research And Development ◽

Hardware Implementation ◽

Detection Method ◽

Low Cost ◽

Experimental Methods ◽

Experimental Results ◽

National Standard ◽

Detection Methods ◽

The Relationship

Owing to the shortcomings of existing series arc fault detection methods, based on a summary of arc volt–ampere characteristics, the change rule of the line current and the relationship between the voltage and current are deeply analyzed and theoretically explained under different loads when series arc faults occur. A series arc fault detection method is proposed, and the software flowchart and principles of the applied hardware implementation are given. Finally, a prototype of an arc fault detection device (AFDD) with a rated voltage of 220 V and a rated current of 40 A is developed. The prototype was tested according to experimental methods provided by the Chinese national standard, GB/T 31143-2014. The experimental results show that the proposed detection method is simple and practical, and can be implemented using a low-cost microprocessor. The proposed method will provide good theoretical guidance in promoting the research and development of an AFDD.

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A Novel Hybrid Strategy for Multimode Operation Mapping and Feature Extraction on Data-Driven Statistical Fault Detection Methods

Volume 13: Design, Reliability, Safety, and Risk ◽

10.1115/imece2018-87417 ◽

2018 ◽

Author(s):

Horacio Pinzón ◽

Cinthia Audivet ◽

Javier Alexander ◽

Melitsa Torres ◽

Marlon Consuegra ◽

...

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Extraction Procedure ◽

Operating Conditions ◽

Data Driven ◽

Superior Performance ◽

Normal Operation ◽

Monitoring Strategy ◽

Network Systems ◽

Multimode Operation

Fault detection and diagnosis schemes based on data-driven statistical modelling are highly dependent on an accurate and exhaustive feature extraction procedure to deliver a superior performance as a monitoring strategy. Otherwise conducted, a deficient feature extraction procedure leads to a monitoring structure widely deviated from normal operating conditions. If an operating state is not identified as it, an increment in false alarm rate would be evidenced whenever the process shifts towards that condition and the monitoring scheme triggers the abnormal condition warning. On the other hand, if two similar operating conditions could not be individualized i.e. to be identified as a single operating state, a lack of sensitivity for minor — yet typical — deviations would render a monitoring strategy with prominent misdetection rates. Although Multimode Operational Mapping requires the proper identification of a finite set of normal process states, it is a challenging task especially for large-scale systems. Its complexity derives from a broad universe of monitoring variables, highly interactuating process units integrated over very dynamic network systems, among others. This is the case of natural gas transmission infrastructure, as it deals with variable upstream production rates, diverse consumption trends from customers, internal processes constrains, merged in a stringent operating scheme. This paper proposes a novel strategy to address the identification and feature extraction of normal conditions on multimode operation systems. The proposed framework uses a segmentation approach based on operator’s knowledge, the Takagi-Sugeno-Kang fuzzy engine and k-means algorithm to characterize the normal operation states of the system. The results show an improvement in the performance of Principal Component Analysis during abnormal conditions detection, in addition an increase on the sensitivity of Hotelling and Q statistics.

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Visual Leakage Inspection in Chemical Process Plants Using Thermographic Videos and Motion Pattern Detection

Sensors ◽

10.3390/s20226659 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6659

Author(s):

Mina Fahimipirehgalin ◽

Birgit Vogel-Heuser ◽

Emanuel Trunzer ◽

Matthias Odenweller

Keyword(s):

Chemical Process ◽

Pattern Detection ◽

Training Data ◽

Detection Methods ◽

Normal Operation ◽

Inspection System ◽

Leakage Detection ◽

Motion Pattern ◽

Motion Patterns ◽

Process Plants

Liquid leakage from pipelines is a critical issue in large-scale chemical process plants since it can affect the normal operation of the plant and pose unsafe and hazardous situations. Therefore, leakage detection in the early stages can prevent serious damage. Developing a vision-based inspection system by means of IR imaging can be a promising approach for accurate leakage detection. IR cameras can capture the effect of leaking drops if they have higher (or lower) temperature than their surroundings. Since the leaking drops can be observed in an IR video as a repetitive phenomenon with specific patterns, motion pattern detection methods can be utilized for leakage detection. In this paper, an approach based on the Kalman filter is proposed to track the motion of leaking drops and differentiate them from noise. The motion patterns are learned from the training data and applied to the test data to evaluate the accuracy of the method. For this purpose, a laboratory demonstrator plant is assembled to simulate the leakages from pipelines, and to generate training and test videos. The results show that the proposed method can detect the leaking drops by tracking them based on obtained motion patterns. Furthermore, the possibilities and conditions for applying the proposed method in a real industrial chemical plant are discussed at the end.

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A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner–Hough Transform

Sensors ◽

10.3390/s21134306 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4306

Author(s):

Kewen Sun ◽

Baoguo Yu ◽

Mireille Elhajj ◽

Washington Yotto Ochieng ◽

Tengteng Zhang ◽

...

Keyword(s):

Hough Transform ◽

Detection Method ◽

Detection Performance ◽

Detection Methods ◽

Energy Concentration ◽

Limited Area ◽

Interference Detection ◽

Transform Method ◽

Cross Term ◽

Time Frequency

This paper develops novel Global Navigation Satellite System (GNSS) interference detection methods based on the Hough transform. These methods are realized by incorporating the Hough transform into three Time-Frequency distributions: Wigner–Ville distribution, pseudo -Wigner–Ville distribution and smoothed pseudo-Wigner–Ville distribution. This process results in the corresponding Wigner–Hough transform, pseudo-Wigner–Hough transform and smoothed pseudo-Wigner–Hough transform, which are used in GNSS interference detection to search for local Hough-transformed energy peak in a small limited area within the parameter space. The developed GNSS interference detection methods incorporate a novel concept of zero Hough-transformed energy distribution percentage to analyze the properties of energy concentration and cross-term suppression. The methods are tested with real GPS L1-C/A data collected in the presence of sweep interference. The test results show that the developed methods can deal with the cross-term problem with improved interference detection performance. In particular, the GNSS interference detection performance obtained with the smoothed pseudo-Wigner–Hough transform method is at least double that of the Wigner–Hough transform-based approach; the smoothed pseudo-Wigner–Hough transform-based GNSS interference detection method is improved at least 20% over the pseudo-Wigner–Hough transform-based technique in terms of the zero Hough-transformed energy percentage criteria. Therefore, the proposed smoothed pseudo-Wigner–Hough transform-based method is recommended in the interference detection for GNSS receivers, particularly in challenging electromagnetic environments.

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