Machine fault diagnois and condition prognois using adaptive Neuro-Fuzzy inference system and classification and regression trees
In this study, classification and regression trees (CART) and adaptive neuro-fuzzy inference systems (ANFIS) will be developed as an effective intelligent system for performing machine fault diagnosis and condition prognosis. CART is known as one of the illustrious techniques of the decision tree induction and used for the purpose of either classification or regression depending on the output variable which is categorical or numerical. CART recursively partitions the entire data into binary descendant subsets which are as homogeneous as possible with respect to the response variables. High effective omputation and reliability are the remarkable advantages of this algorithm. In the second technique, ANFIS is an excellent integration of the adaptive capability of neural networks and the modeling human knowledge ability of fuzzy logic. During the learning process, the parameters of fuzzy membership functions initially determined by experts are adapted to the relationship between the input and output. That combination makes the ANFIS model more systematic and less dependent on the expert knowledge. For implementing the fault diagnosis, CART and ANFIS are combined with another technique so-called feature-based technique. This technique is one of the powerful techniques to represent the raw data as features which are representatives of values indicating the machine condition. By using features, the encountered problem in data transfer and data storage could be effortlessly solved. Feature-based technique consists of data acquisition, data preprocessing, feature representation, feature extraction, feature selection and classifiers. In the proposed system for fault diagnosis, CART is used as a feature selection tool to select pertinent features which can characterize the machine conditions from the whole feature set whilst ANFIS plays a role as a classifier. In order to be evaluated, this system is applied to diagnose the faults of induction motor, which is an indispensable part in several industrial applications. The high performance results indicate that this system offers a potential for machine fault diagnosis. Foretelling the future states of machine has become more and more significant in modern industry. It assists maintainers or system operators in monitoring, inspecting the machines’ operating conditions, and detecting the incipient faults so that they could opportunely perform remedial actions to avoid the catastrophic failures. Furthermore, it enables the scheduled maintenance to be more effective. In this study, the future machines’ operating conditions are predicted by using CART and ANFIS model in combination with time series techniques. These time series techniques consist of methods which are utilized to determine the optimal observations and the steps ahead as the inputs and outputs of predictors, respectively. The trending data of a low methane compressor is used to validate the proposed method. The predicted results show that CART and ANFIS predictors are reliable and promising tools in machine condition prognosis.