[Failure Prediction of Power-Shift Steering Transmission Based on Oil Spectral Analysis with Wiener Process].

The most common methodology used in element concentration measurement and analyzing of wear particles is Atomic emission (AE) spectroscopy. As an indirect measuring method, the oil spectral data is introduced to indicate the performance degradation and the residual life prediction in the reliability evaluation of Power shift steering transmission (PSST). Stochastic methods especially the Wiener process is convenient in solving and analyzing the unitary degradation failure indicated by the oil spectral data. The oil data have been sampled in the real operating condition, and the data set has more than 50 samples taken from PSST. The mean values and time-dependent characteristics of three indicating elements are statistically obtained by the linear regression analysis. The model of the degradation and failure prediction has been proposed based on the Wiener process with the positive drift. For modeling and simulation the software R was used. Therefore, the trend curves of diffusion process with their First Hitting Time have been predicted. Through comparison, the time intervals of condition-based maintenance have been extended as 27 Mh (15.9%). This will save the cost of maintenances by eliminate the preventive maintained cycles. The advantage and novelty of the outcomes presented in the article are that the stochastic process might be applied for predicting the degradation failure occurrence and also for optimizing the maintenance intervals and the cost-benefit. As might be expected, the method can be extended to other cases of wear prediction and evaluation in complex mechanical system.

Research and Evaluation on Wear in Power-Shift Steering Transmission Through Oil Spectral Analysis with RKPCA Method.

The most common methodology used in element concentration measurement and analyzing of wear particles is Atomic emission (AE) spectroscopy. The present paper presents an evaluation method on wear in power-shift steering transmission (PSST). By removing the problematic components which were highly correlated with oil additives, the robust kernel principal component analysis (RKPCA) method and the principal component analysis (PCA) method were accessed to extract the principal components of spectral data for oil samples collected from the life-cycle test of PSST in different stage and to calculate the amount of each principal component and its contribution rate respectively. A comparison between the above mentioned two methods was made to show that RKPCA method has fewer amounts of principal components and higher cumulative contribution rate indicating that RKPCA method acts more effectively in variable dimension reduction due to the outliers and nonlinearity of spectral data. Therefore, the effectiveness of RKPCA method in classification and identification of the wear in friction pairs was demonstrated subsequently. through the correlation analysis between the variable coefficients of RKPCA and metal elements of friction pairs. The demonstration showed that RKPCA functioned precisely in the classification and identification of the wear in friction pairs, and in the evaluation on the wear in PSST. Thereafter, to detect the threshold point where the wear took place, the fuzzy C-means clustering algorithm was introduced to classify the RKPCA eigenvalues, and the results were compared with that of the spectral clustering algorithm. The fuzzy C-means clustering algorithm showed higher sensitivity in detecting the threshold point indicting a more precise evaluation on the wear in PSST. It is clear that the introduction of RKPCA method in wear evaluation, which takes the eigenvalues of spectral data as a critical variable to classify and identify the wear in different friction pairs as well as in the integral PSST configuration, shows better accuracy in wear prediction and will contribute to the reliable determination of life between overhauls and the accurate positioning of worn-out parts. As might be expected, the proposed method can be extended to other cases of wear detection and evaluation in complex mechanical system.

[Health state evaluation of power-shift steering transmission using PCA-AHP methodology based on spectrometric oil analysis].

Spectrometric oil analysis is an important method to study the running state of power-shift steering transmission (PSST). An evaluation model of PSST health state was developed on the basis of the theories of principal component analysis (PCA) and analytic hierarchy process (AHP) using spectrometric oil analysis data. Considering the concept of mechanical equipment and wear elements in spectrometric oil analysis data, the health value was employed to quantitatively describe the running state degree of PSST, and the grades of health state were classified based on the health values. The oil analysis data were studied during the process of choosing principal components. The weight vectors of principal components were obtained by using the AHP method. In the course, the conformation of judgement matrix and the consistency check were also studied. The evaluation model was developed by combining the PCA and AHP methods. This model has been proved to have better accuracy in evaluating the running state of PSST. This work is important for developing state evaluation of PSST.

[Study on no-load running-in wear of power-shift steering transmission based on oil spectrum analysis].

The running-in process wear rule of power-shift steering transmission can be studied conveniently and timely by using spectral analysis of oil. The configuration characteristic and the running-in mechanism of power-shift steering transmission were introduced firstly in the present paper. According to the discussion of running-in wear factors such as load, rotation speed, time, oil temperature, shifting number and original concentration of running-in oil, the wear calculation mode was established. The no-load running-in experiments of two power-shift steering transmissions were done, with different rotation speed and time. Based on the spectrum analysis of experiment result, the function relation between running-in wear and the oil original concentration and running-in speed was obtained, so the no-load running-in process wear calculation mode of power-shift steering transmission was confirmed. Through the experiment of other two power-shift steering transmissions, it was validated that the Cu element concentration can be calculated accurately by the wear calculation mode, which included the parameters such as oil original concentration, running-in speed, running-in time and gear shift alternate time. So the reference to evaluate the running-in quality and to constitute running-in regulations was gained.

[Improved euler algorithm for trend forecast model and its application to oil spectrum analysis].

The oil atomic spectrometric analysis technology is one of the most important methods for fault diagnosis and state monitoring of large machine equipment. The gray method is preponderant in the trend forecast at the same time. With the use of oil atomic spectrometric analysis result and combining the gray forecast theory, the present paper established a gray forecast model of the Fe/Cu concentration trend in the power-shift steering transmission. Aiming at the shortage of the gray method used in the trend forecast, the improved Euler algorithm was put forward for the first time to resolve the problem of the gray model and avoid the non-precision that the old gray model's forecast value depends on the first test value. This new method can make the forecast value more precision as shown in the example. Combined with the threshold value of the oil atomic spectrometric analysis, the new method was applied on the Fe/Cu concentration forecast and the premonition of fault information was obtained. So we can take steps to prevent the fault and this algorithm can be popularized to the state monitoring in the industry.

[Test research on state monitor of PSST by oil spectrometric analysis].

Through the hydraulic lubrication oil spectrometric analysis, we monitored the state of the power-shift steering transmission (PSST) equipped on the track armored vehicle for a long time. Combined with the friction material analysis inside the PSST, based on the concentration analysis of the wear and tear particle inside the oil, the authors estimated the wear and tear state of the PSST and judged if the PSST had the fault hidden trouble. So we can avoid the serious fault, provide the academic and actual basis for RCM and enhance the dependability for the track armored vehicle. The result of examples proves that the characteristic parameter of oil spectrometric analysis has very high stability and veracity. This method has proved effective in fault diagnosis based on oil spectrometric analysis on multifarious power-transmission systems in our army. This method is a breakthrough of the traditional maintainance model and has great useful military values.