The main types of faults studied in the literature are commonly categorized as
electrical faults and mechanical faults. In addition to well known faults, the performance
of a diagnostic algorithm and its operational reliability in harsh environments has been
another concern.
In this work, the reliability of an electric motor diagnosis signal processing algorithm
itself is studied in detail under harsh industrial conditions. Reliability and robustness of
the diagnosis has especially been investigated under 1) potential motor feedback error;
2) noise interference to a diagnosis-relevant system; 3) ease of implementation; and 4)
universal application of diagnostic scheme in industry. Low cost and flexible
implementation strategies are also presented.
1) Signature-based diagnosis has been performed utilizing the speed feedback
information which is used to determine fault characteristic frequency. Therefore,
feedback information is required to maintain high accuracy for precise diagnosis which, in fact, is not the case in a practical industrial environment due to industrial noise
interferences. In this dissertation, the performance under feedback error is analyzed in
detail and error compensation algorithms are proposed.
2) Fault signatures are commonly small where the amplitude is continuously being
interfered with motor noise. Even though a decision is based on the signature, the
detection error will not be negligible if the signature amplitude is within or close to the
noise floor because the boundary noise level non-linearly varies and, hence, is quite
ambiguous. In this dissertation, the effect of noise interference is analyzed in detail and a
threshold design strategy is presented to discriminate potential noise content in diagnosis.
3) The compensating procedure of speed feedback errors and electrical machine
current noise, characteristics which are basically non-stationary random variables,
requires an exhaustive tracking effort. In this dissertation, the effective diagnosis
implementation strategy is precisely presented for digital signal processor (DSP) system
application.
4) Most of the diagnosis algorithms in the literature are developed assuming specific
detection conditions which makes application difficult for universal diagnosis purposes.
In this dissertation, by assuming a sinusoidal fault signal and its Gaussian noise contents,
a general diagnosis algorithm is derived which can be applied to any diagnostic scheme
as a basic tool.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-12-8726 |
| Date | 2010 December 1900 |
| Creators | Choi, Seungdeog |
| Contributors | Toliyat, Hamid A. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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