An integrated approach to bearing fault diagnostics and prognostics

Zhang, Xiaodong; Xu, Roger; Kwan, Chiman; Liang, Steven Y.; Xie, Qiulin; Haynes, L.

doi:10.1109/acc.2005.1470385

Cited by 62 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The adaptive VDHMM Proceedings of the 6th International Conference on Automation, Robotics and Applications, Feb 17-19, 2015, Queenstown, New Zealand matrix, such that the mean of state duration will be best approximated (maximum likelihood). In this section, the adaptive training results are presented based on the training set {T training } consisting of a randomly picked combination: T 2 ,T 3 ,T 5 ,T 8 ,T 9 , T 10 and T 11 .…”

Section: A Adaptive Training Results (With Features F 1 and F 2 )mentioning

confidence: 99%

See 1 more Smart Citation

Adaptive-VDHMM for prognostics in tool condition monitoring

Wong

Gao

2015

2015 6th International Conference on Automation, Robotics and Applications (ICARA)

View full text Add to dashboard Cite

Among techniques used in condition monitoring, those for prognostics are the most challenging. This paper presents a Hidden Markov Model (HMM) based approach for prognostics in TCM. A HMM model usually employs a typical working condition for establishing and verifying the model. However, in tool condition monitoring (TCM), the cutting tool encounters a range of cutting conditions. It is not economical to establish a HMM for every cutting condition. Therefore, an adaptive-Variable Duration Hidden Markov Model (VDHMM) is proposed whereby the training information is adapted to a target test under different cutting conditions to those for establishing the initial model. It is found that with an appropriately selected feature set and state number, the proposed algorithm can significantly reduce the mean absolute percentage error (MAPE).

show abstract

Section: A Adaptive Training Results (With Features F 1 and F 2 )mentioning

confidence: 99%

“…HMMs have been implemented for health-state estimation in quite a number of literatures [3][4][6][7][8][9][10][11]. Although they offer a better connection between the model structure and the physical process than the more commonly used multilayer perceptron, there are two limitations for applying the conventional HMMs to TCM applications.…”

Section: Introductionmentioning

confidence: 99%

Adaptive-VDHMM for prognostics in tool condition monitoring

Wong

Gao

2015

2015 6th International Conference on Automation, Robotics and Applications (ICARA)

View full text Add to dashboard Cite

show abstract

“…Models may be trained on a set of failure modes each with its own signature and associated probabilities in a Hidden Markov Model (HMM) (Kwan, Zhang, Xu, and Haynes, 2003). Zhang, Xu, Kwan, Liang, Xie, and Haynes (2005) developed and implemented an approach where principal components of the input signals were mapped to HMM degradation states. Capturing degradation modes in this manner may not always be scalable across multiple components of a complex system.…”

Section: Prognostic Modelsmentioning

confidence: 99%

Operational Prognostic Model Evaluation

Prakash,

Vuckovic,

Amin

2023

PHMAP_CONF

View full text Add to dashboard Cite

Prognostic analytic models have become a viable way to reduce operational interruptions when sufficient timely data is available. This work describes a set of evaluation metrics which can characterize model performance as a degradation estimate and as a decision enabler. The model accuracy over time is assessed against a correlation with the remaining useful life. This yields both a prediction accuracy and confidence interval. The decision can be based on the level of confidence around the prediction, which is based on both how far into the future the event is predicted and how well the current health and its deterioration is estimated. With an effective means of evaluating prognostic models, better benchmarks can be established to communicate model effectiveness and appropriately schedule routine service.

show abstract

“…Qian, Jiao, Hu and Yan (2007) trained HMMs using a Baum-Welch algorithm to diagnose the type of fault in large scale power transformers. A different approach to diagnosis was suggested by Kwan, Zhang, Xu and Haynes, (2003) and Zhang, Xu, Kwan, Liang, Xie, and Haynes (2005). In both papers, multiple HMMs were created for different failure modes.…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

Review of Markov Models for Maintenance Optimization in the Context of Offshore Wind

Dawid

McMillan

Revie³

2015

PHM_CONF

View full text Add to dashboard Cite

The offshore environment poses a number of challenges to wind farm operators. Harsher climatic conditions typically result in lower reliability while challenges in accessibility make maintenance difficult. One of the ways to improve availability is to optimize the Operation and Maintenance (O&M) actions such as scheduled, corrective and proactive maintenance. Many authors have attempted to model or optimize O&M through the use of Markov models. Two examples of Markov models, Hidden Markov Models (HMMs) and Partially Observable Markov Decision Processes (POMDPs) are investigated in this paper. In general, Markov models are a powerful statistical tool, which has been successfully applied for component diagnostics, prognostics and maintenance optimization across a range of industries. This paper discusses the suitability of these models to the offshore wind industry. Existing models which have been created for the wind industry are critically reviewed and discussed. As there is little evidence of widespread application of these models, this paper aims to highlight the key factors required for successful application of Markov models to practical problems. From this, the paper identifies the necessary theoretical and practical gaps that must be resolved in order to gain broad acceptance of Markov models to support O&M decision making in the offshore wind industry.

show abstract

An integrated approach to bearing fault diagnostics and prognostics

Cited by 62 publications

References 10 publications

Adaptive-VDHMM for prognostics in tool condition monitoring

Adaptive-VDHMM for prognostics in tool condition monitoring

Operational Prognostic Model Evaluation

Review of Markov Models for Maintenance Optimization in the Context of Offshore Wind

Contact Info

Product

Resources

About