Bearings are one of the most common components in automatic machines. Diagnosis and prognosis of their working condition is crucial for minimization of downtime and maintenance costs. Different approaches may be adopted to either solve or mitigate the problem of identifying incipient faults during machinery operations. In this paper, we propose a simple and efficient yet effective method to solve this problem by exploiting the edge-computing capabilities of PLCs. Accelerometer signals are modeled as AutoRegressive (AR) processes whose coefficient are used as features for machine learning, based on logistic regression algorithm (LR), to perform Fault Detection and Isolation (FDI). Estimation and prediction are both implementable on-board the PLC, while machine learning can be carried out remotely, in a cloud computing perspective. The exploitation of AR modelling gives a simple and inherent methodology for feature selection. We apply the procedure to the Case Western Reserve University database, a widely known and used benchmark, to highlight its performance with respect to similar fault recognition techniques.

Condition monitoring of ball bearings using estimated ar models as logistic regression features

Barbieri M.;DIversi R.;Tilli A.
2019

Abstract

Bearings are one of the most common components in automatic machines. Diagnosis and prognosis of their working condition is crucial for minimization of downtime and maintenance costs. Different approaches may be adopted to either solve or mitigate the problem of identifying incipient faults during machinery operations. In this paper, we propose a simple and efficient yet effective method to solve this problem by exploiting the edge-computing capabilities of PLCs. Accelerometer signals are modeled as AutoRegressive (AR) processes whose coefficient are used as features for machine learning, based on logistic regression algorithm (LR), to perform Fault Detection and Isolation (FDI). Estimation and prediction are both implementable on-board the PLC, while machine learning can be carried out remotely, in a cloud computing perspective. The exploitation of AR modelling gives a simple and inherent methodology for feature selection. We apply the procedure to the Case Western Reserve University database, a widely known and used benchmark, to highlight its performance with respect to similar fault recognition techniques.
2019 18th European Control Conference, ECC 2019
3904
3909
Barbieri M.; DIversi R.; Tilli A.
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/732565
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact