Fault diagnosis plays an essential role in rotating machinery manufacturing systems to reduce their maintenance costs. How to improve diagnosis accuracy remains an open issue. To this end, we develop a novel framework through combined use of multi-domain vibration feature extraction, feature selection and cost-sensitive learning method. First, we extract time-domain, frequency-domain, and time-frequency-domain features to make full use of vibration signals. Second, a feature selection technique is employed to obtain a feature subset with good generalization properties, by simultaneously measuring the relevance and redundancy of features. Third, a cost-sensitive learning method is designed for a classifier to effectively learn the discriminating boundaries, with an extremely imbalanced distribution of fault instances. For illustration, a real-world dataset of rotating machinery collected from an oil refinery in China is utilized. The extensive experiments have demonstrated that our multi-domain feature extraction and feature selection can significantly improve the diagnosis accuracy. Meanwhile, our cost-sensitive learning method consistently outperforms the traditional classifiers such as support vector machine (SVM), gradient boosting decision tree (GBDT), etc., and even better than the classification method calibrated by six popular imbalanced data resampling algorithms, such as the Synthetic Minority Over-sampling Technique (SMOTE) and the Adaptive Synthetic sampling method (ADASYN), in terms of decreasing missed alarms and reducing the average cost. Owing to its high evaluation scores and low average misclassification cost, cost-sensitive GBDT (CS-GBDT) is preferred for imbalanced fault diagnosis in practice.

Imbalanced fault diagnosis of rotating machinery via multi-domain feature extraction and cost-sensitive learning

Digital twin (DT) is a key technology for realizing the interconnection and intelligent operation of the physical world and the world of information and provides a new paradigm for fault diagnosis. Traditional machine learning algorithms require a balanced dataset. Training and testing sets must have the same distribution. Training a good generalization model is difficult in an actual production line operation process. Fault diagnosis technology based on the digital twin uses its ultrarealistic, multisystem, and high-precision characteristics to simulate fault data that are difficult to obtain in an actual production line to train a reliable fault diagnosis model. In this article, we first propose an improved random forest (IRF) algorithm, which reselects decision trees with high accuracy and large differences through hierarchical clustering and gives them weights. Digital twin technology is used to simulate a large number of balanced datasets to train the model, and the trained model can be transferred to a physical production line through transfer learning for fault diagnosis. Finally, the feasibility of our proposed algorithm is verified through a case study of an automobile rear axle assembly line, for which the accuracy of the proposed algorithm reaches 97.8%. The traditional machine learning plus digital twin fault diagnosis method proposed in this paper involves some generalization, and thus has practical value when extended to other fields.

Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest

Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis

A novel class imbalance-robust network for bearing fault diagnosis utilizing raw vibration signals

During the last few years, the classification of imbalanced datasets is one of the crucial issues in medical diagnosis since it is related to the distribution of normal and abnormal cases which can potentially affect the performance of the diagnosis system. For solving this problem, various techniques have been designed in order to achieve acceptable quality. Ensemble systems are one of those techniques, and they have proven their ability to be more accurate than single classifier models. Classifier selection is related to the choice of an optimal subset within a pool of classifiers. Selection of classifier can be broadly split into two classes: static and dynamic. This paper proposes a novel set selection scheme for the classification of imbalanced medical datasets. The suggested approach is based on the combination of an improved dynamic ensemble selection called META-DES framework combined with a hybrid sampling method called SMOTE–ENN. The experimental results prove the superiority of the proposed ensemble learning system using three UCI datasets.

SMOTE–ENN-Based Data Sampling and Improved Dynamic Ensemble Selection for Imbalanced Medical Data Classification

Fault diagnosis plays a key role in monitoring manufactured products for the purpose of quality control. Among the several fault diagnosis approaches, knowledge-based fault diagnosis, which uses signals from sensors and machine learning algorithms instead of a priori information, is widely employed to diagnose the status of products. In this paper, we propose a knowledge-based procedure to establish a fault diagnosis model. The model is aimed to diagnose planetary gear carrier packs, which have many fault types and an unbalanced number of samples in the sample classes, using transmission error. In the procedure, the best feature subset that contains the most important features is selected using two different feature selection processes. Several ensemble algorithms are used during the model training process. The imbalance ratio between classes of samples is addressed. The number of weak learners is automatically determined by a genetic algorithm. Finally, the performance of the proposed procedure is validated by comparison with other models trained without applying the proposed procedure. We observed that it is important to incorporate the class imbalance technique in the training process as it reduces the misclassification of faulty products as normal ones. This reduction is important in production quality control.

Fault Diagnosis of Planetary Gear Carrier Packs: A Class Imbalance and Multiclass Classification Problem

The design of multiscale materials and products has necessitated an inductive and robust design approach to ensure satisfying the performance goals for complex engineering problems. Inductive design exploration method is a performance-driven design approach that explores feasible design spaces while considering the effect of uncertainty that leads to performance variability. However, the existing design method suffers from high computational costs for pre-defined sample data, which sacrifices the accuracy of solution spaces. In this study, we present an improved implementation of the inductive design exploration method by applying the active learning algorithm that is mainly used in machine learning techniques. The purpose of this study is to minimize the sampling effort while maintaining reasonable accuracy in the exploration of design spaces, thereby alleviating computational burden. The capabilities of the improved method are highlighted and demonstrated via a design problem of the blast resistant sandwich panel.

Inductive Design Exploration Method with Active Learning for Complex Design Problems

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Fault Diagnosis of Planetary Gear Carrier Packs: A Class Imbalance and Multiclass Classification Problem

Inductive Design Exploration Method with Active Learning for Complex Design Problems