A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

Abstract

Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine learning. In general, the best performing algorithm and the optimal hyper-parameters suitable for each subtask are varied across the characteristics of datasets. Therefore, it is challenging to develop a general diagnostic/prognostic framework that can automatically identify the best subtask algorithms and the optimal involved parameters for a given dataset. To resolve this problem, we propose a new framework based on an ensemble of genetic algorithms (GAs) that can be used for both the fault-type classification and RUL prediction. Our GA is combined with a specific machine-learning method and then tries to select the best algorithm and optimize the involved parameter values in each subtask. In addition, our method constructs an ensemble of various prediction models found by the GAs. Our method was compared to a traditional grid-search over three benchmark datasets of the fault-type classification and the RUL prediction problems and showed a significantly better performance than the latter. Taken together, our framework can be an effective approach for the fault-type and RUL prediction of various machinery systems.

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

“…Nonetheless, GA is an efficient tool for industrial processes optimization. In [83], researchers proposed a new method based on GAs that can be used for both fault-type classification and RUL prediction. The authors in [84] proposed a method based on genetic Nonetheless, GA is an efficient tool for industrial processes optimization.…”

“…The authors in [84] proposed a method based on genetic Nonetheless, GA is an efficient tool for industrial processes optimization. In [83], researchers proposed a new method based on GAs that can be used for both fault-type classification and RUL prediction. The authors in [84] proposed a method based on genetic mutation particle swarm optimization for gear faults diagnosis.…”

A review of prognostics and health management of machine tools

“…Optimization strategies could be used to select the most suitable parameters for maximizing/minimizing the considered metrics, [17]. Even the adopted prognostic algorithm can be selected according to an optimization process, [107]. The meta-herustic algorithms (nature inspired), used even in other fields, could be particularly suitable for this purpose since the highcomplexity of the task, [80,72].…”

A Generic Framework for Prognostics of Complex Systems

“…Still, a problem remains: this addresses only prognostics but not the steps needed before, namely the data pre-processing and diagnostics. This is overcome by Trinh and Kwon [15], who suggest a prognostics method based on an ensemble of genetic algorithms that includes all the steps, from the data pre-processing until the RUL estimation. With this it provides a truly generic framework for prognostics.…”