Application of selected classification schemes for fault diagnosis of actuator systems

Kalisch, Mateusz; Przystałka, P.; Timofiejczuk, Anna

doi:10.15439/2014f158

Cited by 7 publications

(8 citation statements)

References 30 publications

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“…These three main components are basically composed of a set of physical measurement values: flow sensor measurement (F), pressure sensor at the valve inlet (P1), pressure sensor at the valve outlet (P2), liquid temperature (T1), stem displacement (X) and process control external signal (CV). As for the (V1), (V2) and (V3) valves, they can be activated manually in case of any eventuality, thus, the alarm on the actuator must trigger for the closing of the (V1) and (V2) valves, as well as the manual control by the (V3) valve [ 35 , 36 ].…”

Section: Case Studymentioning

confidence: 99%

Development of a Methodology Using Artificial Neural Network in the Detection and Diagnosis of Faults for Pneumatic Control Valves

Andrade

Lopes

Lima

et al. 2021

Sensors

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To satisfy the market, competition in the industrial sector aims for productivity and safety in industrial plant control systems. The appearance of a fault can compromise the system’s proper functioning process. Therefore, Fault Detection and Diagnosis (FDD) methods contribute to avoiding any undesired events, as there are techniques and methods that study the detection, isolation, identification and, consequently, fault diagnosis. In this work, a new methodology that uses faults emulation to obtain parameters similar to the Development and Application of Methods for Diagnosis of Actuators in Industrial Control Systems (DAMADICS) benchmark model will be developed. This methodology uses previous information from tests on sensors with and without faults to detect and classify the situation of the plant and, in the presence of faults, perform the diagnosis through a process of elimination in a hierarchical manner. In this way, the definition of residue signature is used as well as the creation of a decision tree. The whole process is carried out incorporating FDD techniques, through the Non-Linear Auto-Regressive Neural Network Model With Exogenous Inputs (NARX), in the diagnosis of the behavioral prediction of the signals to generate the residual values. Then, it is applied to the construction of the decision tree based on the most significant residue of a certain signal, enabling the process of acquisition and formation of the signature matrix. With the procedures in this article, it is possible to demonstrate a practical and systematic method of how to emulate faults for control valves and the possibility of carrying out an analysis of the data to acquire signatures of the fault behavior. Finally, simulations resulting from the most sensitized variables for the production of residuals that is generated by neural networks are presented, which are used to obtain signatures and isolate the flaws. The process proves to be efficient in computational time and makes it easy to present a fault diagnosis strategy that can be reproduced in other processes.

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Section: Case Studymentioning

confidence: 99%

Development of a Methodology Using Artificial Neural Network in the Detection and Diagnosis of Faults for Pneumatic Control Valves

Andrade

Lopes

Lima

et al. 2021

Sensors

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“…There are applications of machine learning methods to diagnostics of mining equipment and machinery presented in literature [4,5,12,19,30]. The issue of mining industry devices diagnostics was raised among others in the works [7,10,18,25,31].…”

Section: Related Workmentioning

confidence: 99%

“…Decision support systems utilise a data repository of some kind, e.g., a data warehouse [13]. The critical dependence of the decision support system on a data warehouse implementation and an impact of the data quality on decision support is discussed in [17].There are applications of machine learning methods to diagnostics of mining equipment and machinery presented in literature [4,5,12,19,30]. The issue of mining industry devices diagnostics was raised among others in the works [7,10,18,25,31].…”

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confidence: 99%

Decision support and maintenance system for natural hazards, processes and equipment monitoring

Kozielski

Sikora

Wróbel

2016

EiN

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Article citation info: IntroductionCoal mining is a heavy industry that plays an important role on an energy market and employs hundreds of thousands of people. Coal mining is also an industry, where large amount of data is produced but little is done to utilise them in further analysis. Besides, there is a justified need to integrate different aspects of coal mine operation in order to maintain continuity of mining what can be done by introduction of a decision support system (DSS).Currently coal mines are well equipped with the monitoring, supervising and dispatching systems connected with machines, devices and transport facilities. Additionally, there are the systems for monitoring natural hazards (methane-, seismic-and fire hazards) operating in the coal mines. All these systems are provided by many different companies, what causes problems with quality, integration and proper interpretation of the collected data. Another issue is that the collected data are used chiefly for current (temporary) visualisation on boards which display certain places in the mine. Whereas, application of domain knowledge and the results of historical data analysis can improve the operator's and supervisor's work significantly.For example, due to the short-term prognoses about methane concentration, linked with the information about the location and work intensity of the cutter loader, it is possible to prevent emergency energy shutdowns and maintain continuity of mining (the research on this methodology was discussed in [27]). This will enable to increase the production volume and to reduce the wear of electrical elements whose exploitation time depends on the number of switch-ons and switch-offs.It is possible to see the rising awareness of monitoring systems suppliers who has started to understand the necessity to make the next step in these systems development. Therefore, the companies providing monitoring systems seek their competitive advantage in equipping their systems with knowledge engineering, modelling and data analysis methods. This is a strong motivation to consider a DSS presented in this paper.The goal of this paper is to present an architecture of the DISESOR integrated decision support system. The system integrates data from different monitoring systems and contains an expert system module, that can utilise domain expert knowledge, and analytical module, that can be applied to diagnosis of the processes and devices and to prediction of natural hazards. Special focus of the paper is put on the data integration and data cleaning issues, such as outlier detection, realised by means of the data warehouse and the ETL process. The work also contains a more detailed presentation of the prediction module and two case studies showing real applications of the system.The contribution of the paper consists of the architecture of the DISESOR integrated decision support system, its data repository and prediction module. Additionally, it covers the presentation of the issues connected with the preparation and cleaning of the da...

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“…There are applications of machine learning methods to diagnostics of mining equipment and machinery presented in literature [9,10]. Also some initial concepts of the system that processes data streams delivered by the monitoring systems were presented in [11].…”

Section: Related Workmentioning

confidence: 99%

DISESOR - decision support system for mining industry

Kozielski

Sikora

Wróbel

2015

Annals of Computer Science and Information Systems

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Abstract-This paper presents the DISESOR integrated decision support system. The system integrates data from different monitoring and dispatching systems and contains such modules as data preparation and cleaning, analytical, prediction and expert system. Architecture of the system is presented in the paper and a special focus is put on the presentation of two issues: data integration and cleaning, and creation of prediction model. The work contains also a case study presenting an example of the system application.

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Application of selected classification schemes for fault diagnosis of actuator systems

Cited by 7 publications

References 30 publications

Development of a Methodology Using Artificial Neural Network in the Detection and Diagnosis of Faults for Pneumatic Control Valves

Development of a Methodology Using Artificial Neural Network in the Detection and Diagnosis of Faults for Pneumatic Control Valves

Decision support and maintenance system for natural hazards, processes and equipment monitoring

DISESOR - decision support system for mining industry

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