Remanufacturing Assessment Of The Key Technology Based On Waste Parts Degradation Status And Remaining Life Prediction

Mining parts in the special conditions of dusty, corrosion and fast wear, predict the degradation condition and repair and remanufacturing assessment method is becoming enterprises focus concerned. Mining typical parts is mainly crankshaft, gears, bearings and other key module. Generally divided into online service parts and discarded equipment scrap parts. For the online service parts, it is hard to get the condition information to react the recession trend, Existence state of degradation trends and residual life unpredictable problem, brought difficulties to maintenance decision, For the second type parts, becoming the world’s fastest growing components of the waste; the blind remanufacturing will lead to high cost, resource waste, need the enterprise make more scientific and effective health predict method to predict the mining parts defect state and online parts.Research on the mining industry problem, To explore a set of special working condition of the waste components oriented, life prediction and its degradation state recognition to a new way to make assessment system, For health management and remanufacturing recycling waste resources to provide theoretical support.For the first of the online parts problems, plans to do the following research work.At First. Build the recession of performance indicators. For a single feature is susceptible to noise interference, and not sensitive to react the recession of mining waste parts. Principal component analysis method is proposed to fusion the multi-source feature, obtain the first dimension as mining waste parts decline performance indicators, Effectively eliminate the redundancy between the original characteristic information, Through the characterization results verify the index can better reflect the recessionary trend.Second. Through low rank score to selection feature. Improve the accuracy of degradation state. The key of predict the mining waste parts state is how to choice the feature and how to build classification model. Through the study of classification problem, Using the low rank scoring method to selection the feature, the low rank model is to find the best features, to build the low rank score criterion, and then develop a feature selection algorithm based on low rank score. Finally, predict the state of rolling bearing in acceleration of platform experiment, illustrate the feasibility of the idea.Finally. Formulate reasonable maintenance assessment scheme. Put forward a set of degraded to the online service parts for state recognition and maintenance decision-making. Online parts can be divided into three types, continue to use, minor repair, remanufacturing.In view of mining waste parts, plans to do the following research work.At First. For the mining waste parts set up improve the weibull distribution of the residual life prediction model, To provide the basis making for whether its worth to remanufacturing. Based on the historical data. Improve the weibull distribution. Forecast the remaining life based on the SVR and weibull distribution. Through the case analysis, illustrate the feasibility of the idea.Finally. Make reasonable assessment scheme. By building remanufacturing estimation method based on three dimensional structure of the Hall, a new solution for enterprise to make remanufacturing decision. Waste parts divided into three categories: recycling, remanufacturing, recycled recycling. So as to make the waste parts remanufacturing to new. The crankshaft is taken as an example to illustrate the feasibility of the idea.