Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models

2020 ◽  
Vol 13 (1) ◽  
pp. 81-101
Author(s):  
Xiaoling Li ◽  
Shuang shuang Liu
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Large Scale ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Working Environment ◽  
Fault Classification ◽  
Continuous Stirred Tank Reactor ◽  
Reactor Model ◽  
Content Type

PurposeFor the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing. This paper proposes a fault classification algorithm based on Gaussian mixture model (GMM), which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.Design/methodology/approachThe algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy (MYT) decomposition under each normal distribution in the GMM. Then, the weight value of GMM is used to calculate weighted classification value of fault detection and separation, and by comparing the actual control limits with the classification result of GMM, the fault classification results are obtained.FindingsThe experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.Originality/valueThe proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.

A case-based reasoning system for fault detection and isolation: a case study on complex gearboxes

2019 ◽  
Vol 25 (2) ◽  
pp. 213-235 ◽  
Author(s):  
Soumava Boral ◽  
Sanjay Kumar Chaturvedi ◽  
V.N.A. Naikan
Keyword(s):  
Decision Making ◽  
Fault Detection ◽  
Large Scale ◽  
Fault Detection And Isolation ◽  
Decision Making Process ◽  
Case Based Reasoning ◽  
Content Type ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Case Based

Purpose Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar. Design/methodology/approach CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI). Findings The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers. Originality/value The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.

Affective events theory as a theoretical lens for improving the working environment of academics in developing economies

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Majid Ghasemy ◽  
Mahdiyeh Erfanian ◽  
James Eric Gaskin
Keyword(s):  
Higher Education ◽  
Job Satisfaction ◽  
Positive Affect ◽  
Large Scale ◽  
Developing Economies ◽  
Working Environment ◽  
Supervisory Support ◽  
Affective Events Theory ◽  
Content Type ◽  
Affective Events

PurposeThe rapid pace of progress in academic institutions in developing economies has created stressful and relatively toxic workplaces, resulting in different negative organizational outcomes indicating the need to transform universities into healthier academic workplaces. However, a review of the higher education literature in both developed and developing countries shows that the antecedents and consequences of academics' affective states has been a relatively unexplored area. Hence, our study aims at testing basic tenets of Affective Events Theory (AET) in a higher education context to address this issue.Design/methodology/approachThis is a quantitative study which applies CB-SEM methodology in analyzing the collected data from 2,324 academics in Malaysian higher education sector. We analyzed the data using EQS software package.FindingsOur results provided substantial support for the applicability and relevancy of AET in higher education domain. Specifically, welfare and supervisory support were identified as the two work environment features which significantly and equally contribute to academics' job satisfaction. In addition, the results showed that positive affect, in comparison with negative affect, was three times stronger in influencing academics' job satisfaction.Practical implicationsGiven the considerable role of positive affect in our study, higher education policy makers are urged to make relevant policies to transform universities into more emotionally safe workplaces. In addition, policies should be formulated in a way that encourages supervisory support and decreases workloads to ensure that the conflicts in general are reduced among academics.Originality/valueThis work is the first large-scale study testing the main tenets of AET in the higher education context. In addition, it addresses the problem of multivariate normality and solves this problem based on the robust methodology which corrects standard errors and fit indices, thereby providing more precise and unbiased results.

Performance comparisons of bonding box-based contact detection algorithms and a new improvement technique based on parallelization

2016 ◽  
Vol 33 (1) ◽  
pp. 7-27
Author(s):  
Mahmoud Yazdani ◽  
Hamidreza Paseh ◽  
Mostafa Sharifzadeh
Keyword(s):  
Large Scale ◽  
Distinct Element ◽  
Detection Algorithm ◽  
Computational Technique ◽  
Contact Detection ◽  
Content Type ◽  
Running Time ◽  
Spatial Sorting ◽  
Bounding Boxes ◽  
Contact Detection Algorithm

Purpose – The purpose of this paper is to find a convenient contact detection algorithm in order to apply in distinct element simulation. Design/methodology/approach – Taking the most computation effort, the performance of the contact detection algorithm highly affects the running time. The algorithms investigated in this study consist of Incremental Sort-and-Update (ISU) and Double-Ended Spatial Sorting (DESS). These algorithms are based on bounding boxes, which makes the algorithm independent of blocks shapes. ISU and DESS algorithms contain sorting and updating phases. To compare the algorithms, they were implemented in identical examples of rock engineering problems with varying parameters. Findings – The results show that the ISU algorithm gives lower running time and shows better performance when blocks are unevenly distributed in both axes. The conventional ISU merges the sorting and updating phases in its naïve implementation. In this paper, a new computational technique is proposed based on parallelization in order to effectively improve the ISU algorithm and decrease the running time of numerical analysis in large-scale rock mass projects. Originality/value – In this approach, the sorting and updating phases are separated by minor changes in the algorithm. This tends to a minimal overhead of running time and a little extra memory usage and then the parallelization of phases can be applied. On the other hand, the time consumed by the updating phase of ISU algorithm is about 30 percent of the total time, which makes the parallelization justifiable. Here, according to the results for the large-scale problems, this improved technique can increase the performance of the ISU algorithm up to 20 percent.

Support vector machine and K-nearest neighbour for unbalanced fault detection

2014 ◽  
Vol 20 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Ashkan Moosavian ◽  
Hojat Ahmadi ◽  
Babak Sakhaei ◽  
Reza Labbafi
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Experimental System ◽  
Performance Comparison ◽  
Fault Classification ◽  
Support Vector ◽  
Rotating Machines ◽  
Vibration Signals ◽  
Content Type ◽  
Intelligent Approach

Purpose – The purpose of this paper is to develop an appropriate approach for detecting unbalanced fault in rotating machines using KNN and SVM classifiers. Design/methodology/approach – To fulfil this goal, a fault diagnosis approach based on signal processing, feature extraction and fault classification, was used. Vibration signals were acquired from a designed experimental system with three conditions, namely, no load, balanced load and unbalanced load. FFT technique was applied to transform the vibration signals from time-domain into frequency-domain. In total, 29 feature parameters were extracted from FFT amplitude of the signals. SVM and KNN were employed to classify the three different conditions. The performances of the two classifiers were obtained under different values of their parameter. Findings – The experimental results show the potential application of SVM for machine fault diagnosis. Practical implications – The results demonstrate that the proposed approach can be used effectively for detecting unbalanced condition in rotating machines. Originality/value – In this paper, an intelligent approach for unbalanced fault detection was proposed based on supervised learning method. Also, a performance comparison was made between KNN and SVM in fault classification. In addition, this approach gave a high level of classification accuracy. The proposed intelligent approach can be used for other mechanical faults.

An Extended Implementation of Fault Detection in Multi-State Systems Based on Warp Analysis: A Case Study on Natural Gas Transmission Systems in Tropical Regions

2017 ◽  
Author(s):  
Horacio Pinzón ◽  
Cinthia Audivet ◽  
Ivan Portnoy ◽  
Marlon Consuegra ◽  
Javier Alexander ◽  
...  
Keyword(s):  
Natural Gas ◽  
Fault Detection ◽  
Large Scale ◽  
Detection Algorithm ◽  
Operating Conditions ◽  
Underlying Structure ◽  
Transmission Systems ◽  
Original Algorithm ◽  
Tropical Conditions ◽  
Natural Gas Transmission

Natural gas transmission infrastructure is a large-scale complex system often exhibiting a considerable operating states not only due to natural, slow and normal process changes related to aging but also to a dynamic interaction with multiple agents overall having different functional parameters, an irregular demand trend adjusted by the hour, and sometimes affected by external conditions as severe climate periods. As traditional fault detection relies in alarm management system and operator’s expertise, it is paramount to deploy a strategy being able to update its underlying structure and effectively adapting to such process shifts. This feature would allow operators and engineers to have a better framework to address the online data being gathered in dynamic on transient conditions. This paper presents an extended analysis on WARP technique to address the abnormal condition management activities of multiple-state processes deployed in critical natural gas transmission infrastructure. Special emphasis is made on the updating activity to incorporate effectively the operating shifts exhibited by a new operating condition implemented on a fault detection strategy. This analysis broadens the authors’ original algorithm scope to include multi-state systems in addition to process drifting behavior. The strategy is assessed under two different scenarios rendering a major shift in process’ operating conditions related to natural gas transmission systems: A transition between low and high natural gas demand to support hydroelectric generation matrix on severe tropical conditions. Performance evaluation of fault detection algorithm is carried out based on false alarm rate, detection time and misdetection rate estimated around the model update.

Fault detection algorithm used in a magnetic monitoring system of the hydrogenerator

2017 ◽  
Vol 11 (1) ◽  
pp. 63-71 ◽  
Author(s):  
Blagoje M. Babić ◽  
Saša D. Milić ◽  
Aleksandar Ž. Rakić
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Detection Algorithm ◽  
Magnetic Monitoring

Open-plan office post-occupancy evaluation: inquiring Algerian gender specificity

Facilities ◽  
2018 ◽  
Vol 36 (11/12) ◽  
pp. 546-570
Author(s):  
Abdelkrim Benammar ◽  
Karima Anouche ◽  
Hasnia Lesgaa ◽  
Yamina Hamza Cherif
Keyword(s):  
Public Space ◽  
Large Scale ◽  
Working Environment ◽  
Space Environment ◽  
Future Research ◽  
Content Type ◽  
Post Occupancy Evaluation ◽  
Starting Point ◽  
Open Plan ◽  
The Impact

PurposeThis paper aims to examine the impact of an open-plan office (OPO) space organisation on a user’s attitude in the Algerian context; more specifically, it investigates gender differences in the occupants’ perception of such working environment. It, principally, aims to explore the employees’ reaction towards OPO and sees how much such local office type complies with indoor environment quality (IEQ) and psychological comfort.Design/methodology/approachThe theoretical framework of the study is mainly related to environmental psychology referring to the interaction between users and their environment. Post-occupancy evaluation was carried out using exploratory study and questionnaires, followed by statistical analyses. It was performed on a large-scale sample of employees (296 employees) working in recently built OPO situated in Oran (Algeria).FindingsFundamentally, women appear to show more concern regarding comfort. They do not show much reluctance to be mixed with men in a large office space as opposed to more conservative reaction towards mixing up in outdoor public space environment. As for environmental factors (IEQ), indicators have shown the inadequacy of most buildings in terms of thermal, light or noise comfort. The study has also revealed that a majority of users recognise the professional advantages of the OPO, although it is suggested that their preferred type would be the individual office.Originality/valueThe paper provides a concise starting point for future research interested in developing Algerian context OPO design in terms of both indoor environmental and psychological comfort.

scholarly journals Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

2020 ◽  
Vol 66 (4) ◽  
pp. 215-226 ◽  
Author(s):  
Branislav Panić ◽  
Jernej Klemenc ◽  
Marko Nagode
Keyword(s):  
Fault Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Condition Monitoring ◽  
Gaussian Mixture ◽  
Classification Method ◽  
Fault Classification ◽  
Classification Methods ◽  
Bearing Fault ◽  
Model Based

Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring.

Cloud robotic grasping of Gaussian mixture model based on point cloud projection under occlusion

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shifeng Lin ◽  
Ning Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Point Cloud ◽  
Gaussian Mixture ◽  
Working Environment ◽  
Vision Sensor ◽  
Content Type ◽  
Cloud Robotics ◽  
Camera View ◽  
Robot Grasping

Purpose In multi-robot cooperation, the cloud can share sensor data, which can help robots better perceive the environment. For cloud robotics, robot grasping is an important ability that must be mastered. Usually, the information source of grasping mainly comes from visual sensors. However, due to the uncertainty of the working environment, the information acquisition of the vision sensor may encounter the situation of being blocked by unknown objects. This paper aims to propose a solution to the problem in robot grasping when the vision sensor information is blocked by sharing the information of multi-vision sensors in the cloud. Design/methodology/approach First, the random sampling consensus algorithm and principal component analysis (PCA) algorithms are used to detect the desktop range. Then, the minimum bounding rectangle of the occlusion area is obtained by the PCA algorithm. The candidate camera view range is obtained by plane segmentation. Then the candidate camera view range is combined with the manipulator workspace to obtain the camera posture and drive the arm to take pictures of the desktop occlusion area. Finally, the Gaussian mixture model (GMM) is used to approximate the shape of the object projection and for every single Gaussian model, the grabbing rectangle is generated and evaluated to get the most suitable one. Findings In this paper, a variety of cloud robotic being blocked are tested. Experimental results show that the proposed algorithm can capture the image of the occluded desktop and grab the objects in the occluded area successfully. Originality/value In the existing work, there are few research studies on using active multi-sensor to solve the occlusion problem. This paper presents a new solution to the occlusion problem. The proposed method can be applied to the multi-cloud robotics working environment through cloud sharing, which helps the robot to perceive the environment better. In addition, this paper proposes a method to obtain the object-grabbing rectangle based on GMM shape approximation of point cloud projection. Experiments show that the proposed methods can work well.

Automotive Wire Control Steering Sensor Fault Detection Algorithm Research

2015 ◽  
Vol 727-728 ◽  
pp. 708-711
Author(s):  
Zhi Ping Liu
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Large Scale ◽  
Structural Changes ◽  
Detection Algorithm ◽  
Steering System ◽  
Steering Wheel ◽  
Detection Accuracy ◽  
Sensor Fault ◽  
Sensor Fault Detection

This article to cancel after the mechanical connections between steering wheel and steering, wire control steering system security and reliability problems, put forward on the basis of the analytical redundancy software sensor method of wire control steering system. In order to solve the compared with the traditional steering system in terms of reliability and safety of the problems of structural changes, the wire control steering system of the main sensor fault diagnosis methods are studied. In wire control steering system associated with the vehicle dynamics model is established under the premise of hypothesis testing to double adaptive fading Kalman filtering technology as a platform, combined with according to the working state of each sensors to determine fault feature vector, to build the main sensor wire control steering automobile fault diagnosis method of residual error threshold. For fault diagnosis of automobile EPS sensor, the BP neural network is put forward to EPS sensor for auto are introduced in the fault diagnosis. For large-scale wireless sensor networks (WSN), reduce the fault detection accuracy, and larger load of communication problems, according to the spatial and temporal correlation characteristics of sensor nodes, proposes a distributed sensor fault detection algorithm based on cluster. These algorithms for sensor fault detection is of great significance.

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