The prefailure detection of faults in operating plant can effect major rewards in both safety and economy. A successful on-condition maintenance philosophy would pay great dividends particularly in the offshore oil industry where -until recently, only token methods have been employed. Many techniques are available for monitoring mechanical plant and several of these are considered in general terms. Industrial methods are subsequently evaluated on reciprocating compressor and rolling element bearing faults. Bearing fault analysis is considered in two stages. Initially, a series of vibration based techniques are evaluated on a large relatively noise free rotating machine. The techniques of greatest worth carrier spectra, autospectra, time signature analysis and statistical assessments - are then applied to bearings in the hostile environment of a reciprocating machine. It is shown that while discrete faults often produce predictable periodic vibrational patterns, a monitoring system aimed solely at such vibrational phenomena cannot be relied upon. To this end, a diagnostic system must encompass a series of techniques, including carrier spectrum, time signature and statistical analyses. A series of valve and piston faults in reciprocating machines are also studied. By using a number of monitoring techniques, a catalogue of fault characteristics is constructed, and the methods of greatest worth are high-lighted. It is noted that due to the complexities of a reciprocating machine, fault characteristics vary with load, and this must be borne in mind when interpreting the various parameter displays. No single technique can provide a complete cover for all compressor faults, and it is shown that those of greatest worth are acoustic emission, combined pressure and vibration plots, temperature and performance analysis. An indication of compressor temperature and internal cylinder pressure can greatly ease the detection and diagnostic process, and for the latter, bolt load determinations may be a valuable aid.

Robust fault analysis (FA) including the diagnosis of faults and predicting their level of severity is necessary to optimise maintenance and improve reliability of Aircraft. Early diagnosis of faults that might occur in the supervised process renders it possible to perform important preventative actions. The proposed diagnostic models were validated in two experimental tests. The first test concerned a single localised and generalised roller element bearing fault in a permanent magnet brushless DC (PMBLDC) motor. Rolling element bearing defect is one of the main reasons for breakdown in electrical machines. Vibration and current are analysed under stationary and non-stationary load and speed conditions, for a variety of bearing fault severities, and for both local and global bearing faults. The second test examined the case of an unbalance rotor due to blade faults in a thruster, motor based on a permanent magnet brushed DC (PMBDC) motor. A variety of blade fault conditions were investigated, over a wide range of rotation speeds. The test used both discrete wavelet transform (DWT) to extract the useful features, and then feature reduction techniques to avoid redundant features. This reduces computation requirements and the time taken for classification by the application of an orthogonal fuzzy neighbourhood discriminant analysis (OFNDA) approach. The real time monitoring of motor operating conditions is an advanced technique that presents the real performance of the motor, so that the dynamic recurrent neural network (DRNN) proposed predicts the conditions of components and classifies the different faults under different operating conditions. The results obtained from real time simulation demonstrate the effectiveness and reliability of the proposed methodology in accurately classifying faults and predicting levels of fault severity.