Canonical residual based incipient fault detection method for industrial process

Shang, Liangliang; Yan, Ze; Li, Junhong; Qiu, Aibing; Zhang, Hao

doi:10.1109/ccdc49329.2020.9164047

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…It should be noted that CVA has achieved excellent results in many maintenance applications, both in its original or modified forms [14][15][16]. The fundamental characteristic of CVA is that it calculates linear combinations of the past and future values of the system to maximize the correlation between them [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions

Quatrini

Costantino

et al. 2022

Applied Sciences

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In the industrial panorama, many processes operate under time-varying conditions. Adapting high-performance diagnostic techniques under these relatively more complex situations is urgently needed to mitigate the risk of false alarms. Attention is being paid to fault anticipation, requiring an in-depth study of prediction techniques. Predicting remaining life before the occurrence of faults allows for a comprehensive maintenance management protocol and facilitates the wear management of the machine, avoiding faults that could permanently compromise the integrity of such machinery. This study focuses on canonical variate analysis for fault detection in processes operating under time-varying conditions and on its contribution to the diagnostic and prognostic analysis, the latter of which was performed with machine learning techniques. The approach was validated on actual datasets from a granulator operating in the pharmaceutical sector.

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Section: Literature Reviewmentioning

confidence: 99%

Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions

Quatrini

Costantino

et al. 2022

Applied Sciences

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“…W ITH the development and progress of society, the operation state of industrial processes often changes [1], and the whole industrial process continues to move towards intelligence, automation, integration, and so on. For the processes of chemical production, blast furnace ironmaking, and high-precision machining, if the fault detection and identification of the process cannot be carried out in time, it will cause major accidents.…”

Section: Introductionmentioning

confidence: 99%

Industrial Process Fault Detection Based on Entropy Score Contribution Analysis

Liu

Shang

et al. 2022

IEEE Access

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Aiming at the problem of poor fault detection ability in complex industrial processes, this paper proposes a fault detection method of entropy score contribution analysis, which can better determine the number of samples with a high contribution rate and achieve good fault detection results. First, normalize the input data to obtain the Euclidean distance of each line vector; Secondly, the number of samples with a high contribution rate is determined by entropy score contribution and determine the initial threshold; Thirdly, a parameter adaptive strategy is proposed to set the threshold of statistics; Finally, fault detection is carried out on the statistical data, and the size of fault detection rate and false alarm rate is calculated. In the fourth part of this paper, we use this fault detection method to detect the faults from the Tennessee Eastman process. The simulation results show that the method proposed has more advantages than the traditional methods, and can be effectively applied to complex industrial processes.INDEX TERMS Euclidean distance, entropy score contribution, fault detection, false alarm rate, fault detection rate.

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Canonical variate residual analysis for industrial processes fault detection

Li,

Liu

2024

Can J Chem Eng

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With the rapid development of intelligent integration in industrial processes, a challenge emerges. Early failures cannot be detected in a timely manner, potentially leading to significant financial losses. While traditional canonical variate analysis (CVA) methods are effective for dynamic process monitoring, they may lack the flexibility required for early fault detection. To address this challenge, a fault detection method based on canonical variate residual analysis (CVRA) is proposed. CVRA introduces a distinctive residual statistic that preserves critical information about the data. It places heightened focus on the primary components of the data, capturing core features of system changes and enhancing sensitivity to early anomalies. Additionally, by incorporating the geometric properties of the Manhattan distance, it mitigates statistical data errors, thereby improving detection accuracy. Simulation results validate the method's effectiveness in the Tennessee Eastman (TE) process. Furthermore, the successful application of the three‐phase flow facility provides a benchmark for evaluation using real process data.

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Canonical residual based incipient fault detection method for industrial process

Cited by 3 publications

References 19 publications

Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions

Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions

Industrial Process Fault Detection Based on Entropy Score Contribution Analysis

Canonical variate residual analysis for industrial processes fault detection

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