Development of Input Training Neural Networks for Multiple Sensor Fault Isolation

Ren, Shaojun; Si, Fengqi; Cao, Yue

doi:10.1109/jsen.2022.3184078

Cited by 3 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RB approach has also been applied to the neural network technique to address the smearing effect. Ren et al (11,26) introduced the RB approach to AANN and input training neural network (ITNN), and the results showed that these improved methods could effectively inhibit the influence of the smearing effect. Tang et al (16) extended the application of the RB approach to the variational AE model.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction-based stacked sparse auto-encoder for nonlinear industrial process fault diagnosis

Weng,

Ren,

Zhu

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

View full text Add to dashboard Cite

The reconstruction-based (RB) approach can effectively suppress the misdiagnosis problem due to the smearing effect in fault isolation. However, the current exploration of the RB approach for large-scale nonlinear systems is still limited. Therefore, this paper proposes a reliable and effective fault diagnosis method based on a reconstruction-based stacked sparse autoencoder (RBSSAE) for high-dimensional industrial systems. In RBSSAE, a reconstruction-based index achieved by the Steffensen iterative method is developed to check whether the given variable(s) are responsible for the faults efficiently. However, the number of possible faulty variable combinations grows exponentially with the system dimension or actual abnormal variables, causing an unbearable computational burden. Hence, the proposed RBSSAE utilizes a sequential floating forward selection approach to rapidly isolate the most decisive variable combination, meeting a requirement of online fault diagnosis. Finally, the effectiveness of the RBSSAE is verified on a numerical example and a real industrial case.

show abstract

Section: Introductionmentioning

confidence: 99%