Fault prediction is an important topic for the industry as, by providing effective methods for predictive maintenance, allows companies to perform important time and cost savings. In this paper we describe an application developed to predict and explain door failures on metro trains. To this end, the aim was twofold: first, devising prediction techniques capable of early detecting door failures from diagnostic data; second, describing failures in terms of properties distinguishing them from normal behavior. Data pre-processing was a complex task aimed at overcoming a number of issues with the dataset, like size, sparsity, bias, burst effect and trust. Since failure premonitory signals did not share common patterns, but were only characterized as non-normal device signals, fault prediction was performed by using outlier detection. Fault explanation was finally achieved by exhibiting device features showing abnormal values. An experimental evaluation was performed to assess the quality of the proposed approach. Results show that high-degree outliers are effective indicators of incipient failures. Also, explanation in terms of abnormal feature values (responsible for outlierness) seems to be quite expressive.There are some aspects in the proposed approach that deserve particular attention. We introduce a general framework for the failure detection problem based on an abstract model of diagnostic data, along with a formal problem statement. They both provide the basis for the definition of an effective data pre-processing technique where the behavior of a device, in a given time frame, is summarized through a number of suitable statistics. This approach strongly mitigates the issues related to data errors/noise, thus enabling to perform an effective outlier detection. All this, in our view, provides the grounds of a general methodology for advanced prognostic systems.

Fault detection and explanation through big data analysis on sensor streams

Rullo P.;
2017

Abstract

Fault prediction is an important topic for the industry as, by providing effective methods for predictive maintenance, allows companies to perform important time and cost savings. In this paper we describe an application developed to predict and explain door failures on metro trains. To this end, the aim was twofold: first, devising prediction techniques capable of early detecting door failures from diagnostic data; second, describing failures in terms of properties distinguishing them from normal behavior. Data pre-processing was a complex task aimed at overcoming a number of issues with the dataset, like size, sparsity, bias, burst effect and trust. Since failure premonitory signals did not share common patterns, but were only characterized as non-normal device signals, fault prediction was performed by using outlier detection. Fault explanation was finally achieved by exhibiting device features showing abnormal values. An experimental evaluation was performed to assess the quality of the proposed approach. Results show that high-degree outliers are effective indicators of incipient failures. Also, explanation in terms of abnormal feature values (responsible for outlierness) seems to be quite expressive.There are some aspects in the proposed approach that deserve particular attention. We introduce a general framework for the failure detection problem based on an abstract model of diagnostic data, along with a formal problem statement. They both provide the basis for the definition of an effective data pre-processing technique where the behavior of a device, in a given time frame, is summarized through a number of suitable statistics. This approach strongly mitigates the issues related to data errors/noise, thus enabling to perform an effective outlier detection. All this, in our view, provides the grounds of a general methodology for advanced prognostic systems.
Anomaly detection; Big data; Fault detection; Outlier explanation; Sensor data
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/302898
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