FL offers the ability to deal with uncertainties in maintenance and scheduling processes and it has been used to improve the performance of fault detection and prediction in mechanical systems. It has been widely implemented in fault diagnosis applications because of its advantages in approximating reasoning and in linguistic knowledge implementation.
In mechanical equipment monitoring tasks, fuzzy logic theory was applied in cases where precise mathematical models were unavailable or too complex, but where there was still some vague, subjective and empirical knowledge related to the problem under investigation (Wang and Lei, 2001). The existing knowledge is generally constructed as a set of fuzzy relationships or fuzzy rules on which the overall fuzzy system is based. These rules or fuzzy relationships can be constructed based on information supplied by human experts.
Chapter 3 – Review of Artificial Intelligence (AI) Systems in Fault Diagnosis 65 In cases where only partial or incomplete fuzzy rules could be supplied by human experts where a set of problems or system input-output data were available, then it was deemed preferable to extract fuzzy relationships or fuzzy rules from the system- based data and combine the data, where possible, with human knowledge and experience. The combination could then be used to construct a complete and relevant set of fuzzy rules.
An early application of FL in fault diagnosis was investigated by Goode and Chow (1995), in which a hybrid neural-fuzzy fault detector was used to detect motor faults. In the investigation, a neural-fuzzy fault detector was used to monitor the condition of the motor bearingswear and the stator winding insulation failure. Fuzzy IF-THEN rules for bearing wear classification by the detector were constructed heuristically for three ranges of membership: low, medium and high. The trained neural-fuzzy fault detector was able to provide accurate fault detection results and could also provide the heuristic reasoning behind the fault detection process and the actual motor fault conditions.
Another early application of fuzzy logic (FL) in fault diagnosis was proposed by Goddu et al. (1998) in which a fuzzy logic-based method was used to interpret vibration signals from an electric motor in order to diagnose bearing faults. Spectrum data of the vibration signal was entered into the fuzzy decision system and a valid fault diagnosis result obtained. It was suggested that incorporation of neural networks or genetic algorithms with fuzzy logic to improve the capabilities of the fuzzy decision system, would be beneficial.
Vicente et al. (2001) presented a work on an automatic diagnosis system for detection and classification defects in rolling bearings using fuzzy logic. The measured vibration signals were analysed using spectral and statistical techniques. The variables used as inputs for the fuzzy system included: radial load, shaft speed, kurtosis, skewness and RMS. The designed system was able to classify three types of pre-established defects in rolling element bearings which operated under several shaft speeds and load conditions. The results showed that the designed system was able to diagnose 97% of the test database to distinguish between normal conditions
and a fault case. The system achieved 95% accuracy in the classification of fault cases of normal, pit corrosion, and scratched condition.
Miguel and Blazquez (2005) applied fuzzy logic in a model-based diagnosis application for a DC motor controller. Fuzzy logic was used to handle the uncertainty of the system model, noise and others variables that reduce the reliability and robustness of the fault diagnosis method. The FL fault detection and isolation system was successfully applied in a laboratory in which the uncertainty caused by disturbances and modelling errors was reduced.
Celik and Bayir (2007) studied the application of a complementary fuzzy logic system in fault diagnosis of an internal combustion engine. The fuzzy rules of the system were constructed by using theoretical knowledge, expert knowledge and the experimental results. The accuracy of the fuzzy logic classifier was tested by experimental studies which were performed under differing fault conditions. Using the developed fault diagnosis system, ten general faults which were observed in the internal combustion engine were successfully diagnosed in real time.
Wu and Hsu (2009) studied the development of a gear fault identification scheme using vibration signals with fuzzy logic inference and discrete wavelet transforms (DWT) for an experimental gear-set system. A proposed scheme that employed the combination of signal feature extraction using discrete wavelet transform techniques and fault identification using fuzzy logic inference was investigated. The fuzzy logic inference was proposed to develop the diagnostic rules of the database in the fault identification system. The experimental works were performed to evaluate the effect of fault diagnosis in a gear-set system under various operation conditions. The experimental results showed that the proposed fault diagnosis scheme was effective as it increased the accuracy in gear fault identification of the gear-set system. The aim of using FL was to overcome difficulties in the fault diagnosis of rotating machinery in a complex and noisy environment and to reduce the need for the knowledge of an experienced technician.
Saravanan et al. (2009) used a fuzzy classifier that was obtained from intuitive information and related domain knowledge of fault characteristics for a bevel gear
Chapter 3 – Review of Artificial Intelligence (AI) Systems in Fault Diagnosis 67 fault diagnosis scheme. A decision tree was used in selecting the best statistical features that could discriminate the fault condition of the gearbox vibration signals. It was followed by the formation of a rule set from the extracted features and then given to a fuzzy classifier for the fault classification process. The results of the fuzzy classifiers were found to be encouraging.