Modified multi-scale symbolic dynamic entropy and fuzzy broad learning-based fast fault diagnosis of railway point machines

Liu, Junqi; Wen, Tao; Xie, Guo; Cao, Yuan

doi:10.1093/tse/tdac065

Cited by 4 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…C. L. Philip Chenʹs team combined the Takagi-Sugeno (TS) fuzzy system with the BLS to introduce a novel fuzzy neural network model termed Fuzzy Broad Learning (FBLS) [29]. FBLS maintains the core structure of BLS while integrating the TS fuzzy subsystem in lieu of BLSʹs feature nodes and eliminating the sparse self-encoder used for weight fine-tuning in the BLS feature layer, thereby simplifying the architecture [30]. This modification not only enables FBLS to retain the rapid computational properties of BLS, but also enhances the modelʹs classification capability.…”

Section: Fuzzy Broad Learning System (Fbls)mentioning

confidence: 99%

Bearing Fault Diagnosis Method Based on Multi-Domain Feature Selection and the Fuzzy Broad Learning System

Wu,

Zhang,

Qin

et al. 2024

Processes

View full text Add to dashboard Cite

In recent years, the Broad Learning System (BLS) has been acknowledged for its potential to revolutionize traditional artificial intelligence methods due to its short training time, strong interpretability, and simple structure. In the evolution of BLS, Prof. C. L. Philip Chen’s team introduced the Fuzzy Broad Learning System (FBLS) by replacing the feature nodes of BLS with fuzzy subsystems, thereby further reducing the training time. However, the traditional FBLS, with its straightforward structure, falls short in achieving higher fault diagnosis accuracy when handling raw vibration signals. This paper presents a bearing fault diagnosis approach employing multi-domain feature selection and the fuzzy broad learning system (MS-FBLS), aiming to enhance the diagnostic accuracy of FBLS through multi-domain feature selection. Primarily, a set of 49 features spanning time domain, frequency domain, time-frequency domain, and entropy values is extracted from the original vibrational signals. This combination builds a 49-dimensional multidomain feature set that exploits the information behind the input data as much as possible, thus compensating for the lack of feature extraction capability in FBLS. Afterward, the Random Forest algorithm assesses the significance of all features, leading to a reordering of the multidomain feature set based on their respective importance levels. Ultimately, the reorganized multidomain feature set is then fed into the FBLS, enabling the identification of various failure states within the bearing. The experimental validation conducted on the rolling bearing fault simulation test bed showcased that, in comparison to the traditional FBLS, the MS-FBLS method not only elevates diagnostic accuracy by 23.46%, but also substantially enhances diagnostic speed. These results serve as comprehensive evidence affirming the effectiveness of the method.

show abstract

Section: Fuzzy Broad Learning System (Fbls)mentioning

confidence: 99%

Bearing Fault Diagnosis Method Based on Multi-Domain Feature Selection and the Fuzzy Broad Learning System

Wu,

Zhang,

Qin

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…Whole-machine-related parameters. Recently, sound and vibration signals during point machine operation have gained attention for fault detection [12,13]. While the throwing time and in-machine temperature have been monitored traditionally, they are less commonly considered for fault detection.…”

mentioning

confidence: 99%

Fault Detection for Point Machines: A Review, Challenges, and Perspectives

Hu,

Tang,

Tan

et al. 2023

Actuators

View full text Add to dashboard Cite

Point machines are the actuators for railway switching and crossing systems that guide trains from one track to another. Hence, the safe and reliable behavior of point machines are pivotal for rail transportation. Recently, scholars and researchers have attempted to deploy various kinds of sensors on point machines for anomaly detection and/or incipient fault detection using date-driven algorithms. However, challenges arise when deploying condition monitoring and fault detection to trackside point machines in practical applications. This article begins by reviewing studies on fault and anomaly detection in point machines, encompassing employed methods and evaluation metrics. It subsequently conducts an in-depth analysis of point machines and outlines the envisioned intelligent fault detection system. Finally, it presents eight challenges and promising research directions along with a blueprint for intelligent point machine fault detection.

show abstract