An integrated framework via key-spectrum entropy and statistical properties for bearing dynamic health monitoring and performance degradation assessment

Yao, Renhe; Jiang, Hongkai; Yang, Chunxia; Zhu, Hongxuan; Liu, Chaoqiang

doi:10.1016/j.ymssp.2022.109955

Cited by 36 publications

(13 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yacine et al [141] used the method based on EMD to obtain appropriate IMFs, and then used frequency enhanced energy operator spectrum to extract features. Yao et al [142] proposed a new spectrum analysis technology called key-spectrum, and then defined a new health index for key-spectrum entropy to quantify health status. To sum up, fault diagnosis of mechanical equipment based on spectrum analysis technology is widely used and continuously improved and developed.…”

Section: Other Spectrum Analysis Techniques and Brief Summarymentioning

confidence: 99%

Review of spectrum analysis in fault diagnosis for mechanical equipment

Wang,

Sun

2023

Eng. Res. Express

View full text Add to dashboard Cite

Various mechanical equipment play a crucial role, and their health or status may affect efficiency and safety seriously. Spectrum analysis of the corresponding signal has been widely used to diagnose the fault in the past decades. The diagnosis method based on spectrum analysis technology covers almost all aspects of mechanical fault diagnosis. However, there is a lack of review of diagnostic methods of spectrum analysis technologies in the field of mechanical equipment fault diagnosis. In order to fill this gap, this paper reviews the spectrum analysis technology in mechanical equipment diagnosis in detail. First of all, in order to let the researchers who are in contact with spectrum analysis technology for the first time quickly understand this field, the principles of spectrum are systematically sorted out, including spectrum, cepstrum, energy spectrum, power spectrum, higher-order spectrum, Hilbert spectrum, marginal spectrum, envelope spectrum, singular spectrum and so on. Furthermore, the characteristics of corresponding spectrum analysis technologies are summarized, and their advantages and disadvantages are analyzed and compared. High-quality references in recent ten years are cited for illustration to enhance persuasiveness. Finally, the prospect of spectrum analysis technology is summarized, and the future development trend of spectrum analysis technology is pointed out. It is believed that the joint diagnosis of fault severity, variable speed fault diagnosis, combined with deep learning and multiple spectrum analysis technologies should be given more attention in the future. This paper is expected to provide a comprehensive overview of mechanical fault diagnosis based on spectrum analysis theory, and help to develop corresponding spectrum analysis technologies in practical engineering.

show abstract

Section: Other Spectrum Analysis Techniques and Brief Summarymentioning

confidence: 99%

Review of spectrum analysis in fault diagnosis for mechanical equipment

Wang,

Sun

2023

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…Among them, PDA is both important for diagnosis and prognostics [7,8]. The main task and connotation of PDA is to establish a health assessment indicator to track the degradation trend of components [7][8][9]. The general and standardized steps for constructing a health indicator (HI) are feature extraction, feature selection, and feature fusion [7,10].…”

Section: Introductionmentioning

confidence: 99%

“…Commonly used feature extraction indicators include time domain-based ones such as kurtosis, root mean square (RMS), shape indicator (SI), etc [11,12]; frequency domain-based ones, such as frequency domain average (FDA), frequency center, etc [13,14]. Among them, kurtosis, RMS, and their variants are the most used statistical indicators [7,9,15]. Kurtosis is sensitive to early fault features [7,8,16] and can be used for early fault detection which is considered a prerequisite for PDA.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel performance degradation assessment method for rotating machinery based on the fault information and the dynamic simulation

Zhang,

Qin

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The performance degradation assessment (PDA) of key components such as gears and rolling bearings is the core technology of prognostics and health management for rotating machinery. Most existing PDA methods may have two deficiencies: (1) the assessment indicator constructed does not consider capturing fault characteristics directly; (2) feature selection is generally based on the measured data of different fault levels, which is difficult to obtain in actual processes; moreover, the selection results lack universality and are difficult to extend to other equipment. To address these issues, this paper proposes a novel PDA method based on fault information and dynamic simulation. First, anomaly detection is performed using four well-known indicators in combination with Mahalanobis distance. Secondly, fault identification is performed using envelope spectrum analysis on anomaly signals to determine the fault type, e.g., gear fault and outer race fault. Thirdly, based on the fault type information, the candidate feature set including fault-domain indicators is selected based on the established dynamic simulation signals to obtain a preliminary assessment vector for the first stage. The stability of the fault domain indicators which capture fault characteristics directly is tested through actual measured normal data. It is used as the second stage of selecting to obtain the assessment vector. Finally, the PDA indicator is calculated based on the assessment vector and Mahalanobis distance. Four experiment case studies demonstrate the proposed PDA method can effectively isolate faults with different defect sizes as well as track the whole performance degradation. The above analysis indicates that the proposed PDA method is expected to be used for the actual rotating machinery.

show abstract

“…Rolling bearing is a key component in modern frontier equipment, its health condition has an important impact on the operating condition of industrial equipment (Yan et al, 2022a(Yan et al, , 2022bYao et al, 2023). Nowadays, rolling bearing health management technology has been widely used in many fields such as automobiles, ships, large units, aerospace, petrochemical, and so on (Feng et al, 2023;Xuejiao et al, 2023;Zhang et al, 2023) and has generated a very considerable economic value.…”

Section: Introductionmentioning

confidence: 99%

Bearing performance degradation assessment using adversarial fusion convolutional autoencoder based on multi-source information

Wang,

Zhou,

Wen

et al. 2023

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Bearing operation states will directly determine the performance of the equipment; thus, monitoring operation status and degradation indicators is the key to ensuring continuous and healthy operation of the equipment. However, most of the research uses single-source information data, which makes it difficult to model when dealing with multi-source information, complex data distribution, and noise. In this paper, a bearing performance degradation assessment method based on multi-source information is proposed to comprehensively utilize the data signals of different structures, spaces, types, and sources. First, the adversarial fusion convolutional autoencoder is constructed for obtaining the degradation index of the bearing, while the adversarial learning strategy is applied to achieve the effect of enhancing the robustness and sensitivity of the degradation indicators extracted by the network. Then the degradation index is input into the support vector data description to determine the fault anomalies of the degradation index adaptively and the fuzzy c-means algorithms to obtain the final rolling bearing performance degradation evaluation results. Through the verification results of two experiment datasets, it is found that the proposed model can achieve accurate evaluation and quantitative analysis of the performance degradation process of bearings. As a result, the entire network ensures the reconstruction accuracy of normal samples while simultaneously stretching the reconstruction error of abnormal samples to achieve accurate monitoring of degradation onset.

show abstract

An integrated framework via key-spectrum entropy and statistical properties for bearing dynamic health monitoring and performance degradation assessment

Cited by 36 publications

References 44 publications

Review of spectrum analysis in fault diagnosis for mechanical equipment

Review of spectrum analysis in fault diagnosis for mechanical equipment

A novel performance degradation assessment method for rotating machinery based on the fault information and the dynamic simulation

Bearing performance degradation assessment using adversarial fusion convolutional autoencoder based on multi-source information

Contact Info

Product

Resources

About