Modeling and analysis of failure propagation of mechanical system with multi-operation states using high-level Petri net

Wu, Jianing; Yan, Shaoze; Gao, RX

doi:10.1177/1748006x13519621

Cited by 2 publications

(3 citation statements)

References 20 publications

(39 reference statements)

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“…The linear correlation coefficient test method [26] is used to conduct hypothesis testing. When significance level 𝛼 = 0.1, the critical value of the correlation coefficient 𝜌 0 = )] is linear, and the component fault follows the hypothesis distribution.…”

Section: Modeling Of Component Fault Probabilitymentioning

confidence: 99%

Key fault propagation path identification of CNC machine tools based on maximum occurrence probability

Zhang¹,

Zhou²,

Yang³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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In order to revise the deviation caused by ignoring the dynamic character of fault propagation in traditional fault propagation path identification methods, a method based on the maximum occurrence probability is proposed to identify the key fault propagation path. Occurrence probability of fault propagation path is defined by dynamic importance, dynamic fault propagation probability and fault rate. Taking the fault information of CNC machine tools which subject to Weibull distribution as an example, this method has been proven to be reasonable through comparative analysis. Result shows that the key fault propagation path of CNC machine tools is not unique, but changes with time. Before 1000 hours, key fault propagation path is electrical component (E) to mechanical component (M); after 1000 hours, key fault propagation path is auxiliary component (A) to mechanical component (M). This change should be taken into account when developing maintenance strategies and conducting reliability analysis.

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Section: Modeling Of Component Fault Probabilitymentioning

confidence: 99%

Key fault propagation path identification of CNC machine tools based on maximum occurrence probability

Zhang¹,

Zhou²,

Yang³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

View full text Add to dashboard Cite

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“…Therefore, the ways in which system components interact will affect the way failures propagate within the subsystems and across subsystem boundaries. 7 These are called failure propagations. These failure propagations may have consequences for many components, thus new failures may again be produced.…”

Section: Problemmentioning

confidence: 99%

“…In this case, the existing failure model cannot explain such a problem. Besides this, in order to explain failure propagation in the production system, we can use a number of diagnosis methods such as: propagation graph, 7 temporal chronic, 10 and Petri net. 11 These approaches use historical production data to locate components that are possible fault origins.…”

Section: Literature Reviewmentioning

confidence: 99%

Fault diagnosis for the complex manufacturing system

Nguyen

Duong

Zamaï

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part O:

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Present manufacturing systems are equipped with sensors that provide a basis for real-time monitoring and diagnosis; however, placement of these sensors is constrained by the the functions that they perform and the system's structure. Moreover, sensors cannot be placed across all components in the equipment due to significant data challenges. This results in non-observable components, which limit our ability to support effective real-time monitoring and fault diagnosis initiatives. Consequently, product quality drifts found during inspection often result in unscheduled breakdown of all equipment involved in respective production operation. This situation becomes more complex for automated manufacturing lines, where success depends on our ability to capitalize maximum production capacities. This paper proposes a methodology that exploits historical data over unobserved equipment components to reduce the search space of potential faulty components, followed by a more accurate diagnosis of failures and causes. The purpose is to improve the effectiveness and efficiency of both the real-time monitoring of potential faulty components and the diagnosis of causes. In the proposed approach, we use a logical diagnosis approach to reduce the search space of suspected equipment in the production flow, which is then formulated as a Bayesian network. The proposed approach computes the risk priority for suspected equipment with corresponding factors (such as human factor and recipe), using joint and conditional probabilities. The objective is to quickly and accurately localize the possible fault origins in real-time and support effective corrective maintenance decisions. The key advantages offered by this approach are: (i) reduced unscheduled equipment breakdown duration, and (ii) stable production capacities, required for success in highly competitive and automated manufacturing systems. Moreover, this is a generic methodology and can be deployed on fully or semi-automated manufacturing systems.

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Modeling and analysis of failure propagation of mechanical system with multi-operation states using high-level Petri net

Cited by 2 publications

References 20 publications

Key fault propagation path identification of CNC machine tools based on maximum occurrence probability

Key fault propagation path identification of CNC machine tools based on maximum occurrence probability

Fault diagnosis for the complex manufacturing system

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