An Integrated Prognostics Method for Failure Time Prediction of Gears Subject to the Surface Wear Failure Mode

Zhao, Fuqiong; Tian, Zhigang; Liang, Xihui; Xie, Mingjiang

doi:10.1109/tr.2017.2781147

Cited by 30 publications

(14 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The American Gear Manufacturing Association (AGMA) classified 36 failure mechanisms of gears into seven categories: wear, scuffing, surface fatigue, plastic flow, cracking, bending fatigue, and breakage. The gear failure is classified into two categories: lubricated and non-lubricated [2,[67][68][69][70]. Sliding wear, scoring, plastic flow, surface fatigue, cracking, fracture, and bending fatigue are the most common causes of gear failure.…”

Section: List the Possible Failure Modesmentioning

confidence: 99%

Failure Modes and Effect Analysis of Gear Pair

Jangra¹

2022

IJCET

View full text Add to dashboard Cite

show abstract

Section: List the Possible Failure Modesmentioning

confidence: 99%

Failure Modes and Effect Analysis of Gear Pair

Jangra¹

2022

IJCET

View full text Add to dashboard Cite

show abstract

“…The information updating process of Kalman filter consists of two parts: the time updating process as in (3)(4) and the measurement updating process as in (5-7):…”

Section: A Kalman Filtermentioning

confidence: 99%

“…A large number of mechanical equipment is of high reliability and long life, such as fuel pumps [1]- [2], steering gears [3], bearings [4][5], etc. They are widely deployed in the fields of aerospace, network communication, transportation, environmental protection and other domains.…”

Section: Introductionmentioning

confidence: 99%

Degradation Pattern Identification and Remaining Useful Life Prediction for Mechanical Equipment Using SKF-EN

et al. 2020

View full text Add to dashboard Cite

Due to the influence of working environment and stress, the failure degradation process of mechanical equipment is characterized by non-stationarity and uncertainty. The degradation pattern of a large number of mechanical equipment obeys the process from steady degradation to accelerated degradation. The rotating machinery wear results in gradual degradation in the early stage while rough contact surface and spall of equipment leads to accelerated degradation stage. However, the change-point of the degradation patterns are uncertain which leads to the ambiguity of the degradation pattern transition and uncertainty of the remaining life prediction model. Aiming at the problems of ambiguity of the pattern transition and uncertainty of the prediction model, the SKF-EN method is proposed in this paper. In this method the real-time data samples are processed through Switching Kalman filters, and the current degradation pattern is identified based on the posterior probability between the two filters of steady degradation and accelerated degradation. When the filters confidence indicates the entering of accelerated degradation pattern, the Elastic Net is used to model the degradation trajectory and predict its life in real time. The method was verified with an airborne fuel pump and rolling element bearings, and it realized the real-time identification of the degradation pattern and effective prediction of the remaining useful life.

show abstract

“…In (Zhao et al, 2018), an integrated method has been presented that combines Bayesian update with Archard's wear model. The presented method is unique to the given wear model.…”

Section: Physicsmentioning

confidence: 99%

A New Hybrid Prognostic Methodology

Eker¹,

Camci

Jennions

2019

IJPHM

View full text Add to dashboard Cite

Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications, this is very rarely the case, and hence the chosen method’s performance can deteriorate quite markedly from optimal. For this reason, a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer-term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available.

show abstract

An Integrated Prognostics Method for Failure Time Prediction of Gears Subject to the Surface Wear Failure Mode

Cited by 30 publications

References 48 publications

Failure Modes and Effect Analysis of Gear Pair

Failure Modes and Effect Analysis of Gear Pair

Degradation Pattern Identification and Remaining Useful Life Prediction for Mechanical Equipment Using SKF-EN

A New Hybrid Prognostic Methodology

Contact Info

Product

Resources

About