Performance degradation of metal rubber seal under high temperature and pressure

Seals are crucial components of mechanical devices; seal degradation limits the performance of mechanical systems and even causes accidents. In this work, we used a complex sealing test system to conduct long-term leakage experiments of metal rubber seals as a function of temperature and pressure. The leakage rate was the performance degradation indicator. We developed an experimental performance degradation pattern by linear regression. We combined the leakage pattern of metal rubber seals with leakage theory and found that the increase in the equivalent leakage channel height is the immediate reason for metal rubber seal performance degradation, itself fundamentally attributable to the time-dependent plasticity caused by material creep.

A new intelligent fault diagnosis and prognosis method for wind turbine doubly-fed induction generator

Condition Monitoring and fault-prognosis approaches are typical methods to reduce the energy production cost and Wind Turbine downtime. In this paper, a new CM combinatory system and fault prognosis are proposed based on an adaptive threshold, feature-level fusion, and new degradation indicator and the CM operation is based on a new index Symptom of Degeneration crossing of an adaptive threshold. Also, a new adaptive threshold is proposed based on the fuzzy rules and WT operation point. Fault prognosis is conducted with the Least-Squares Support-Vector Machine method, and Particle Swarm Optimization is employed for the optimum selecting of the wavelet Kernel function and the SVM parameters. The proposed technique is compared with other methods and the simulation results illustrate the PSO-LS-SVM superiorities. The effectiveness of the proposed prognostic structure is evaluated using a WT test-rig prototype. The experimental results demonstrate that the Condition-Based Maintenance is improved by the proposed structure and the RUL is predicted before serious damage occurrences.

Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression

High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.

Failure Threshold Determination of Rolling Element Bearings Using Vibration Fluctuation and Failure Modes

One of the challenges in predicting the remaining useful life (RUL) of rolling element bearings (REBs) is determining a proper failure threshold (FT). In the literature, the FT is usually assumed to be a constant value of an extracted feature from the vibration signals. In this study, a degradation indicator was extracted to describe damage to REBs by applying principal component analysis (PCA) to their run-to-failure data. The relationship between this degradation indicator and the vibration peak was represented through a joint probability distribution using statistical copula models. The FT was proposed as a probability distribution based on the fluctuation increase in the vibration trend. A set of run-to-failure tests was conducted. Applying the proposed method to this dataset led to various FTs for the different failure modes that occurred. It is shown that, for inner race degradation, a higher FT can be assumed than for rolling element degradation. This could help extend the lives of REBs regarding the degrading elements. A dataset for an industrial machine was also analyzed and it is shown that the proposed model estimated a reasonable and proper FT in an actual case study.