An ANFIS algorithm for forecasting overall equipment effectiveness parameter in total productive maintenance

Total Productive Maintenance (TPM) is a successful technique used for corrective, preventive and predictive maintenance policies. It is important in identifying the success and overall effectiveness of the manufacturing process for long term economic viability of business. Overall equipment effectiveness (OEE) is commonly used and well-accepted metric for TPM implementation in many manufacturing industries. In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to obtain forecasted results for OEE parameter in TPM through some predetermined inputs such as availability, performance efficiency and rate of quality. Triangular type of membership functions was determined as low, medium, and high for each input parameter in the ANFIS model. Fuzzy c-means clustering algorithm was used for determining of the membership degrees of membership functions for each input parameter. This study is important to forecast the risk by OEE in the TPM. With the predicted results of OEE performance an appropriate maintenance strategy can be developed and the production can be improved. This can also help reducing the risk level of breakdowns or failures at any critical equipment