An Effective Quality Machine Learning to Predict Industry Equipment Failures

This study focuses on applying quality machine learning techniques to manage, anticipate and stop industrial equipment failures. Failures of the equipment can result in expensive downtime, lost production, and safety issues. Moreover, several machine learning methodologies, such as supervised, unsupervised, reinforcement and deep learning are already proposed over the various dataset collections to classify and predict the equipment failure and prevention accordingly. There is still a need for generalizability across many equipment types and industries on the application of machine learning for equipment failure prediction and prevention. In order to tackle such challenges, the proposed model includes a Quality learning method (Q-learning) for equipment failure prediction. By incorporating data skewness into the Q-learning algorithm, the reinforcement learning agent can learn to select maintenance strategies that are more appropriate for the given distribution of equipment failure rates. Data on equipment physical operation will be gathered and identify the skewness in the data, which will then be used as training data along with preprocessing to train the Q-learning model. Later to assess their performance with respect to the effectiveness of the learned maintenance policy by taking into account the distribution of equipment failure rates and selecting strategies that are more appropriate for the given situation and found an improved performance.