Analysis Of Time Domain Analysis

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After the real time data is collected from the sensors, the first important step for condition monitoring and faults detection is to develop efficient signal analysis algorithm that can be used to extract useful information from raw data for further diagnostic and prognostic purpose. Several useful algorithms that widely used for faults detection purpose will be discussed in this section which includes time-domain analysis, frequency domain analysis, time-frequency analysis and wavelet transform. These techniques are the fundamental of health monitoring methods. Time-domain analysis Most of the signals from sensors are in time series form for example vibration signal, temperature signal and current signal of an induction motor. Time-domain …show more content…

The signals after TSA indicate the feature information related to the faults which need to be detected. The TSA is usually applied to detect faults with known repetitive frequency of desired signal (Mcfadden and Toozhy 2000). For example, faults in rolling bearings, shaft and gears with known defect frequencies. TSA is given by follow equation: Where s ̅(t) represents the signal, T is the averaging period and N is the number of samples for averaging. Frequency-domain analysis Frequency-domain analysis is the most common signal processing algorithm used for online condition monitoring which is based on the transformed signal in frequency domain. Many electrical and mechanical faults of motor generate “noise” signals at other frequencies which can be determined from knowledge of motor parameters. These fault signals in presence of large noise appear in different sensor signals such as current, vibration and flux. By using frequency analysis, different types of faults information can be provided. Because some faults results in similar faults frequencies, to differentiate them, other information is required. Compared with time-domain analysis, frequency-domain analysis is easier to identify and isolate certain frequency components of interest. The fast Fourier transform (FFT) (Burgess 1988) is the most widely used conventional diagnosis technique to

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