A stochastic Functional Model based method for random vibration based robust fault detection under variable non–measurable operating conditions with application to railway vehicle suspensions

• Published in: Journal of sound and vibration Vol. 466; p. 115006
• Main Authors: Aravanis, T.-C.I., Sakellariou, J.S., Fassois, S.D.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 03-02-2020; Elsevier Science Ltd
• Subjects: Computer simulation; Convexity; Data–driven methods; Fault detection; Fault diagnosis; Functional models; Optimization; Principal components analysis; Railway vehicles; Random vibration; Risk levels; Statistical time series methods; Stochastic models; Subspace methods; Suspension systems; System dynamics; Variable operating conditions; Vibration; Vibration based methods; Vibration control; Vibration measurement; Railway vehicles; Fault detection; Variable operating conditions; Data–driven methods; Functional models; Vibration based methods; Statistical time series methods
• ISSN: 0022-460X; 1095-8568
• DOI: 10.1016/j.jsv.2019.115006

The problem of random vibration based robust fault detection under variable and non–measurable Environmental and Operating Conditions (EOCs) is considered, and a novel stochastic Functional Model (FM) based method is postulated. It is a data–driven method, of the Statistical Time Series (STS) type, and aims at overcoming the well known drawbacks of available methods by achieving high detection performance while eliminating their drawbacks, such as the need for measurable EOCs, for measurement of a high number of vibration signals for proper training, for subjective judgement in selecting method parameters, and for high dimensional non–convex optimization procedures. The method is based on representing the system dynamics, under any set of EOCs, in a proper feature space, within which the healthy dynamics are represented by a proper healthy subspace constructed via a Functional Model. Fault detection is then based upon determining, at a certain risk level, whether or not the current dynamics resides within the healthy subspace. The method's assessment is achieved via simulation results with a case study pertaining to fault detection in a railway vehicle suspension under variable payload, with high detection performance, clearly exceeding that of an alternative Principal Component Analysis (PCA) based method.