An effective health indicator based on two dimensional hidden Markov model

Li, Lin; Ming, Tingfeng; Liu, Shuyong; Zhang, Shuai

doi:10.1007/s12206-017-0301-3

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Wang et al [224] proposed a method of bearing DA based on hierarchical Dirichlet process (HDP)-HMM, in which HDP was used to obtain the state number of equipment in operation and HMM was used to evaluate performance degradation. To establish the index with an obvious trend, Li et al [225] proposed the negative log likelihood probability based on the two-dimensional HMM as the bearing performance degradation index, showing the sensitivity to weak defects. Liu et al [226] proposed a bearing DA method based on orthogonal local preserving projection (OLPP) and continuous HMM.…”

Section: Traditional Ml-based Methodsmentioning

confidence: 99%

Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

Zhao

et al. 2021

Chin. J. Mech. Eng.

104

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Prognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry. With the development of artificial intelligence (AI), especially deep learning (DL) approaches, the application of AI-enabled methods to monitor, diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry. However, there is still a gap to cover monitoring, diagnosis, and prognosis based on AI-enabled methods, simultaneously, and the importance of an open source community, including open source datasets and codes, has not been fully emphasized. To fill this gap, this paper provides a systematic overview of the current development, common technologies, open source datasets, codes, and challenges of AI-enabled PHM methods from three aspects of monitoring, diagnosis, and prognosis.

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Section: Traditional Ml-based Methodsmentioning

confidence: 99%

Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

Zhao

et al. 2021

Chin. J. Mech. Eng.

104

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“…To address the above issues, the technology of fusing multi-dimensional features through machine learning to construct improved HIs has received widespread attention from researchers. Li et al [30] combined local preserved projection and 2D hidden Markov model to establish a HI of negative logarithmic likelihood probability. Atamuradov et al [31] developed a fusion algorithm based on minimum feature divergence weight to obtain representative HI after feature screening.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear characterization of enhanced and generalized Hjorth’s feature space for bearing condition monitoring

Li,

Wang,

et al. 2023

Meas. Sci. Technol.

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The degradation assessment of rolling bearings provides a reasonable maintenance plan for the safe operation of mechanical equipment. The general strategy for bearing condition monitoring is to construct a health indicator (HI) to characterize different degradation stages. A preferable HI that can sensitively detect initial faults and track machine degradation is crucial to developing timely maintenance strategies for mechanical equipment to avoid catastrophic accidents. However, many developed and reported HIs are still insensitive to early faults, resulting in delayed maintenance schedules. To identify the incipient defects as early as possible, a novel HI constructed by nonlinear characterization of enhanced and generalized Hjorth’s feature space based on extended probability entropy is proposed in this paper. Firstly, the time-frequency spectral amplitude modulation helps to enhance the characteristics of the original signal with the amplitude editing in the time-frequency domain. Then, three new features of generalized Hjorth’s parameter combinations are designed and combined with other similar feature combinations to construct a high-dimensional enhanced and generalized Hjorth’s feature space. On this basis, a set of low-dimensional sensitive features is obtained by nonlinearly characterizing high-dimensional features through extended probability entropy after these features are standardized. Finally, a novel HI is developed by calculating the distance between the minimum volume ellipse (MVE) center of the low-dimensional feature subspace based on nonlinear characterization and the low-dimensional feature vector of the real-time monitoring signal. The performance of the proposed approach is verified in three cases, whose experimental results indicate that the proposed HI is more sensitive and significant in detecting early faults compared to some current HIs.

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“…Voice and image data are independent of time, so there is no need to consider previous states of the system. However, with the expansion of the application in other identification fields, such as fault diagnosis [29,30] and equipment health monitoring [31,32], HMM has several shortcomings: only the current state is considered by HMM during recognition process; thus, a high recognition rate is very difficult to realize due to the lack of previous states of the system. Fault prediction is also difficult to implement.…”

Section: Introductionmentioning

confidence: 99%

A hybrid fault prediction method for control systems based on extended state observer and hidden Markov model

Yang

Hai

Cui³

et al. 2022

Asian Journal of Control

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Fault diagnosis and prediction for complex control systems rely either on the collection of rich data for training neural networks or on the system models and prior knowledge of faults. These methods are difficult to apply directly in complex integrated systems due to the large uncertainties in practical scenarios. A new fault diagnosis and prediction technique that is based on extended state observer (ESO) and a hidden Markov model (HMM) for control systems is proposed in this paper. Real‐time and predictive information that is obtained by ESO of the active disturbance rejection control (ADRC) is utilized to improve the HMM method for the fault prediction of control systems with large uncertain disturbances. The proposed approach realizes a high recognition rate with a small demand for data, and the dependence on the system model is weak without prior knowledge of faults. Fault prediction of the control system output can be realized without additional sensors. The proposed solution is evaluated in simulations of an asynchronous servo motor control system against the traditional control method and the ADRC control. The results indicate that the proposed method performs well in fault prediction and outperforms the traditional method in terms of control when disturbances and failures occur.

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An effective health indicator based on two dimensional hidden Markov model

Cited by 7 publications

References 15 publications

Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

Nonlinear characterization of enhanced and generalized Hjorth’s feature space for bearing condition monitoring

A hybrid fault prediction method for control systems based on extended state observer and hidden Markov model

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