模式识别(心理学)
支持向量机
判别式
计算机科学
熵(时间箭头)
特征向量
人工智能
稳健性(进化)
方位(导航)
特征选择
工程类
算法
数据挖掘
生物化学
化学
物理
量子力学
基因
作者
Zhe Li,Longlong Li,Runlin Chen,Yanchao Zhang,Yahui Cui,Ningqiang Wu
出处
期刊:Measurement
[Elsevier]
日期:2023-11-22
卷期号:224: 113907-113907
被引量:4
标识
DOI:10.1016/j.measurement.2023.113907
摘要
To avoid production interruptions and equipment damage caused by rolling bearing failure, this study presents a novel diagnosis scheme applicable to both condition monitoring and fault recognition. In detail, to fully exploit feature information, a modified Hierarchical Time-Shift Multi-scale Amplitude-Aware Permutation Entropy (MHTSMAAPE) is put forward to map the raw vibration signal into a high-dimensional vector space of features. This method is developed from the AAPE algorithm and integrates the time-shift procedure and modified hierarchical analysis, extracting more feature details while ensuring entropy stability. Afterwards, the averaged value of the feature vector is regarded as an indicator to assess the condition of rolling bearing, this indicator exceeds a specific value indicates the rolling bearing steps into faulty. Then, the Unsupervised Discriminative Feature Selection (UDFS) algorithm is first introduced to directly pick fault-sensitive features from the vector space, relieving the calculation burden of the fault recognition task. The fault identification algorithm is based on Harris Hawks Optimization-Joint Opposite Selection optimized for Support Vector Machine (HHO-JOS-SVM) to improve classification accuracy. Finally, the proposed scheme fused by MHTSMAAPE, UDFS, and HHO-JOS-SVM is validated in two different cases with four common evaluation metrics, respectively. The experimental results demonstrate its reliability and robustness in rolling bearing condition assessment and fault recognition.
科研通智能强力驱动
Strongly Powered by AbleSci AI