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Wavelet analysis is used in identifying a continuous-time system with dead time. First, we present a dead-time identification technique by using the wavelet transform of the cross-specta of input/output (I/O) data. Secondly, we present a method for obtaining the frequency response of the system to be considered by directly using the wavelet transform of the auto-/cross-eorrelation functions of the I/O data. Thirdly, we determine the parameters in the transfer function by using the curve-fitting in the least squares sense. The adventage of our identification procedure is that the estimates of parameters can be obtained separately from the delay-time estimate. Finally, we demonstrate the feasibility and validity of our procedure through experiments with an actual boiler process.

Diesel engines have been widely used as electrical power sources for small islands and also as mechanical power sources for ships under the isolated circumstances. Therefore, failure diagnosis is of great importance. The paper pays a great attention to the abnormal sealing of exhaust valves, because it occurs frequently and tends to grow into a serious failure.Based on the data obtained from artificial failure tests using an actual engine, pattern recognition theory was applied to the time series of the longitudinal vibration signal measured by accelerometers on cylinder heads. The paper proposed a method to discriminate between failures in sealing of exhaust valves and in fuel injection timing. Effectiveness of the method was finally verified with experiments.

There have been many approaches in the field of stabilizing control of inverted pendulum system. In many cases, however, linealization technique for small deviation from equilibrium point has been adopted to apply LQ theory or MRACS to the inverted pendulum system. These algorithms are not necessarily satisfactory for a class of non-linear and time-varying systems such as the inverted pendulum system.In this paper, we propose a new MRACS technique with predictor network which is applicable for the non-linear and time-varying system, inverted pendulum system. This predictor-network-based MRACS can also achieve any convergence characteristics.