This paper describes a successful but challenging application of data mining in the railway industry. The objective is to optimize maintenance and operation of trains through prognostics of wheel failures. In addition to reducing maintenance costs, the proposed technology will help improve railway safety and augment throughput. Building on established techniques from data mining and machine learning, we present a methodology to learn models to predict train wheel failures from readily available operational and maintenance data. This methodology addresses various data mining tasks such as automatic labeling, feature extraction, model building, model fusion, and evaluation. After a detailed description of the methodology, we report results from large-scale experiments. These results clearly show the great potential of this innovative application of data mining in the railway industry.

This article investigates metrics to assess and compensate for the degradation of the adhesive layer of surface-bonded piezoceramic transducers for structural health-monitoring applications. Capacitance, resonance frequency, and modal damping parameters are derived from admittance curves using a lumped parameter model to monitor the degradation of the transducer adhesive layer. A pitch-catch configuration is then used to discriminate the effect of bonding degradation on actuation and sensing. It is shown that below the first mechanical resonance frequency of the piezoceramic transducers, the degradation causes a decrease in the amplitude of the transmitted and received signals, while above resonance, in addition to a decrease in the amplitude of the transmitted and received signals, a linear phase shift is observed. A signal-correction factor is proposed to adjust signals based on adhesive degradation evaluated using the measured modal damping. The benefits of the signal-correction factor are demonstrated in the frequency domain for both the A 0 and S 0 modes.

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FMEA (Failure Mode and Effects Analysis) is a systematic method to characterize product and process problems. As a standard document, an FMEA is produced during the design of a product or system. However, once a system is deployed, the corresponding FMEA is rarely validated and updated. This is mainly due to the lack of method to validate and update FMEA. This paper argues that historical maintenance and operational data could be used to help address this problem. Building on data mining and database techniques, the paper introduces a FMEA validation and update technique. The proposed technique derives statistics from real world historical operation and maintenance data and uses these statistics to update key FMEA parameters such as Failure Rate and Failure Mode Probability. The paper then shows how the validated FMEA can be used with data mining for fault detection and identification of root contributing component for a given failure mode or failure effect. The paper presents the developed methodology for FMEA validation and experimental results for fault identification. The results show that the updated FMEA can provide more reliable and accurate information that could benefit the decision-making process and improve maintenance efficiency.