A remaining useful life prediction and maintenance decision optimal model based on Gamma process

Aiming at the practical problem of maintenance decision-making, the remaining useful life (RUL) prediction method and the maintenance decision optimization model are studied emphatically. Firstly, the condition space model based on Gamma degradation process is established, according to the characteristics of the degradation process of the equipment condition. Then the RUL expectancy is predicted by this model, and the RUL probability density function of the equipment can be got. Finally, this model is validated by the data obtained from the roller bearing life test. The maintenance decision model is established with the minimum cost as the objective, the maintenance decision is optimized, and the RUL prediction and maintenance decision are realized. the example proves the validity and feasibility of this model

Optimal Two Dimensional Preventive Maintenance Policy Based on Asymmetric Copula Function

For some kinds of products, the consumers have strict requirements to the reliability of these products in the based warranty period. Then the manufacturer is inclined to provide the two-dimensional preventive maintenance policy to take the usage degree of the product into account. As a result, two-dimensional preventive maintenance policy in the warranty period has recently obtained increasing attention from manufacturers and consumers. In this paper, we focused on the optimization of based warranty cost and proposed a new expected based warranty cost model considering the two-dimensional imperfect preventive maintenance policy from the perspective of the manufacture. Asymmetric copula function was applied to modeling the failure function of the product. And the optimal two-dimensional preventive maintenance period was obtained by minimizing based warranty cost. At last, numerical examples are given to illustrate the proposed models, of which the results prove the model effective and validate

Maintenance decision-making based on remaining useful life considering economy optimal targets and its application

Components in engineering systems are degenerated due to different conditions. In order to avoid serious loss, the components need to be replaced before failure occurs to provide the intended and safe operation of the systems. Therefore, the maintenance decision-making method based on remaining useful life considering different optimal targets was presented in this paper, and the purpose of this method was to find an optimal choice of maintenance actions to be performed on a selected group of machines for engineering systems. Then, the optimal maintenance interval based on the minimum expected cost rule was discussed, and the arithmetic reduction of intensity model was introduced to describe the influence of remaining useful life. Finally, a full life experiment of gearbox was given to demonstrate the preferred decision-making with the influence of the remaining useful life

Engine remaining useful life prediction based on trajectory similarity

The traditional remaining useful life prediction methods need to study the mechanism failure of equipment and the vibration signals can easily be submerged by the noise in the actual operation, in order to solve these problems, the methods of Trajectory similarity based prediction (TSBP) and condition monitoring based on lubricant information are proposed in this paper. The gradient model of lubricant data information which is processed by principal component analysis (PCA) is used to monitor equipment status. Additionally, degradation trajectory abstraction procedure and similarity evaluation procedure are studied in detail. Finally, the both studies are combined for the research of engine remaining useful life prediction and case study proves the simplicity and effectiveness of this method

Application of EMD-WVD and particle filter for gearbox fault feature extraction and remaining useful life prediction

Fault feature extraction and remaining useful life (RUL) prediction are important to condition based maintenance (CBM). In order to realize the fault feature extraction of gearbox vibration signal presenting nonlinear and non-Gaussian, the integration of empirical mode decomposition (EMD) and Wigner-Ville distribution (WVD) are proposed in this paper. Taking the kurtosis as standard, the WVD is applied to some IMFs with larger kurtosis to calculate the time-frequency distribution, with an effective suppress on mode mixing and the cross-term interference. Afterwards, particle filter (PF) with the state space model based on Wiener process is proposed to predict the RUL of gearbox considering degradation feature, gearbox teeth wear and nonlinear and non-Gaussian system. The gearbox life cycle test shows that the EMD-WVD method can extract the valued characteristics of vibration signal accurately, and the particle filter can provide an effective way to predict the RUL of gearbox

Hybrid residual fatigue life prediction approach for gear based on Paris law and particle filter with prior crack growth information

Gear has been widely used in the modern industry, and the gear reliability is important to the driving system, which makes the residual fatigue life prediction for a gear crucial. In order to realize the residual fatigue life of the gear accurately, a hybrid approach based on the Paris law and particle filter is proposed in this paper. The Paris law is usually applied to predict the residual fatigue life, and accurate model parameters allow a more realistic prediction. Therefore, a particle filtering model is utilized to assess both model parameters and gear crack size simultaneously. As a data-driven method, particle filter describes the dynamical behavior of model parameters updating and gear crack growth, whereas the Paris law, as a model-based method, characterizes the gear’s crack growth according to the physical properties. The integration of the Paris law and particle filter is proposed as a hybrid approach, which is suitable for nonlinear and non-Gaussian systems, and can update the parameters online and make full use of the prior information. Finally, case studies performed on gear tests indicate that the proposed approach is effective in tracking the degradation of gear and accurately predicts the residual gear fatigue life

A Diagnosis Method for the Compound Fault of Gearboxes Based on Multi-Feature and BP-AdaBoost

Gearbox is an important structure of rotating machinery, and the accurate fault diagnosis of gearboxes is of great significance for ensuring efficient and safe operation of rotating machinery. Aiming at the problem that there is little common compound fault data of gearboxes, and there is a lack of an effective diagnosis method, a gearbox fault simulation experiment platform is set up, and a diagnosis method for the compound fault of gearboxes based on multi-feature and BP-AdaBoost is proposed. Firstly, the vibration signals of six typical states of gearbox are obtained, and the original signals are decomposed by empirical mode decomposition and reconstruct the new signal to achieve the purpose of noise reduction. Then, perform the time domain analysis and wavelet packet analysis on the reconstructed signal, extract three time domain feature parameters with higher sensitivity, and combine them with eight frequency band energy feature parameters obtained by wavelet packet decomposition to form the gearbox state feature vector. Finally, AdaBoost algorithm and BP neural network are used to build the BP-AdaBoost strong classifier model, and feature vectors are input into the model for training and verification. The results show that the proposed method can effectively identify the gearbox failure modes, and has higher accuracy than the traditional fault diagnosis methods, and has certain reference significance and engineering application value

Use of medications and lifestyles of hypertensive patients with high risk of cardiovascular disease in rural China.

Hypertension, with a global prevalence of 40%, is a risk factor for cardiovascular diseases (CVD). We conducted an exploratory study in Zhejiang China to understand the prevention of CVD among hypertensive patients with a 10 year CVD risk of 20% or higher. We assessed current practices in a rural 'township hospital' (a primary care facility), and compared them with international evidence-based practice.A questionnaire survey was conducted to examine the use of modern drugs (antihypertensive drugs, statins and aspirin) and traditional drugs, compliance to medications and lifestyle among 274 hypertensive patients aged 40-74, with a CVD risk of 20% or higher (using the Asian Equation).The majority (72%) were diagnosed with hypertension at township hospitals. Only 15% of study participants used two anti-hypertensive drugs, 0.7% took statin and 2.9% aspirin. Only 2.9% combined two types of modern drugs, while 0.4% combined three types (antihypertensives, statins and aspirin). Herbal compounds, sometimes with internationally rarely recommended drugs such as Reserpine were taken by 44%. Analysis of drug adherence showed that 9.8% had discontinued their drug therapy by themselves. 16% had missed doses and these were on less anti-hypertensive drugs than those who did not (t=-5.217, P=0.003). Of all participants, 28% currently smoked, 39% drank regularly and only 21% exercised frequently. The average salt intake per day was 7.1 (±3.8) g, while the national recommended level is 6g.The study revealed outdated and inadequate treatment and health education for hypertensive patients, especially for those who have high risk scores for CVD. There is a need to review the community-based guidelines for hypertension management. Health providers and patients should make a transition from solely treating hypertension, towards prevention of CVD. Health system issues need addressing including improving rural health insurance cover and primary care doctors' capacity to manage chronic disease patients

Cardiovascular (CVD) risks factors of the 8182 rural residents aged 40 to 75 years old in Zhejiang, China.

<p>Significantly higher than the population with low risk of CVD: ^a(χ² = 42.478, P<0.001), ^b(t = 38.733, P<0.001), ^c(t = 44.737, P<0.001), ^d(t = 21.040, P<0.001), ^e(t = 2.800, P = 0.005), ^f(χ² = 8.416, P = 0.004), ^g(t = 4.730, P<0.001), ^h(χ² = −91.588, P<0.001), ⁱ(Z = −35.261, P<0.001).</p