Investigations on generalized Hjorth's parameters for machine performance degradation assessment

Yan, Tongtong; Wang, Dong; Xia, Tangbin; Peng, Zhike; Xi, Lifeng

doi:10.1016/j.ymssp.2021.108720

Cited by 8 publications

(10 citation statements)

References 58 publications

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“…This can be confirmed again from figure 26 that kurtosis cannot track the evolution of the rolling bearing fault. The above description is consistent with the viewpoint of [7]. In the implementation process of the above four cases, some results can be further analyzed and explained.…”

Section: Discussionsupporting

confidence: 74%

“…(2) Based on the assessment curve of case 1, case 2, and case 3 in this paper, it can be found that the characteristics and laws of components' degradation are not strictly monotonically increasing. The assessment curve in existing [6][7][8][9]14] also exhibits this phenomenon. The reason may lie in that there are three nonlinear mappings: One is the mapping between defect growth patterns and runtime, the other is the mapping between defect size and fault features, and the third is the mapping between fault features and the PDA indicator.…”

Section: Discussionmentioning

confidence: 65%

“…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%

“…Fault diagnosis, performance degradation assessment (PDA), and remaining useful life prediction are the core technologies of PHM. 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].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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.

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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.

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Section: Discussionsupporting

confidence: 74%