Detecting deviations and faults when Sample Clauses

Detecting deviations and faults when comparing with nominal behaviour To detect deviations in machine components, where the external environment and applications contribute less than in the previous example, Volvo CE would like to compare the operation variables with an average or nominal behaviour gained from the complete machine fleet of the same model. The nominal behaviour is analysed and following sent out to the machines in the form of thresholds etc. depending on usage profile. This help to calibrate the fault detection routines on each machine. In the Smart Vortex project, an example where the pressure build up in transmission clutches will be studied. If the pressure build up is not correct, the results will be slippage which must be avoided. Any deviations from the nominal behaviour can help to indicate that slippage might occur in the future. The following data stream is needed to study the gear shift from forward first gear to forward second gear:  Show the average of the 10 latest gear shifts F1-F2 for the time data of control valve downstream pressure starting at gear switch and ending 1 s after clutch speed difference is zero. Given this type of data stream for all possible gear switch combinations:  Show the group of 10% machines of a given model that deviates the “most” from the others in terms of all the pressure build up data streams A similar case is the deviation of gear shift comfort among machines. This demands on-board Fast Fourier Transformation (FFT) filtering:  Show the average of the 10 latest gear shifts F1-F2 for the frequency range of 1-3 Hz for FFT filtered time data of longitudinal accelerometer starting at gear switch and ending 1 s after clutch speed difference is zero.  Show the group of 10% machines of a given model that deviates the “most” from the others in terms of all the FFT filtered longitudinal acceleration data streams
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Detecting deviations and faults when comparing with nominal behaviour The deviating groups of machines could even be hierarchic, if the deviating groups in the last section were retrieved from each context group. In this way, the deviating machines can be studied in more detail using interactive fault diagnosis: • Given a deviating machine with respect to transmission clutch pressure build up data stream, initiate a remote interactive fault diagnosis session. Operator secures safety. • Show in real-time in a flow diagram the pressure of all transmission clutches with sampling frequency 50 Hz. At the same time display the reference pressure from transmission Electronic Control Unit (ECU) • The mechanic demands actuation of valves according to given scheme and/or manually. Show deviation of valve position to reference signal. • Play sound of mobile microphone with upper frequency limit from database. Machine operator places microphone at suitable position.

Related to Detecting deviations and faults when

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