Reliability evaluation based on the subspace similarity using vibration signal

In order to overcome the shortcomings of traditional reliability analysis based on large samples and statistical methods, a reliability evaluation method of the turboprop engine based on subspace similarity is proposed. The state matrix of vibration signals of the turboprop engine is obtained by the wavelet packet. The subspace of state matrix is constructed by kernel principal component analysis (KPCA). The kernel of the subspace inner product matrix is obtained based on singular value decomposition (SVD). The normalized first nuclear protagonist is treated as the operational reliability index. The method is validated by using the measured data of turboprop engines form an aircraft machinery factory. And it shows that the proposed method is reasonable and feasible, which provides an effective means for the reliability evaluation of machinery under the small sample condition

Trends in condition monitoring of pitch bearings

The value of wind power generation for energy sustainability in the future is undeniable. Since operation and maintenance activities take a sizeable portion of the cost associated with offshore wind turbines operation, strategies are needed to decrease this cost. One strategy, condition monitoring (CM) of wind turbines, allows the extension of useful life for several parts, which has generated great interest in the industry. One critical part are the pitch bearings, by virtue of the time and logistics involved in their maintenance tasks. As the complex working conditions of pitch bearings entail the need for diverse and innovative monitoring techniques, the classical bearing analysis techniques are notsuitable. This paper provides a literature review of several condition monitoring techniques, organized as follows: first, arranged according to the nature of the signal such as vibration, acoustic emission and others; second, arranged by relevant authors in compliance with signal nature. While little research has been found, an outline is significant for further contributions to the literature.Postprint (published version

Time Series Adaptive Online Prediction Method Combined with Modified LS-SVR and AGO

Fault or health condition prediction of the complex systems has attracted more attention in recent years. The complex systems often show complex dynamic behavior and uncertainty, which makes it difficult to establish a precise physical model. Therefore, the time series of complex system is used to implement prediction in practice. Aiming at time series online prediction, we propose a new method to improve the prediction accuracy in this paper, which is based on the grey system theory and incremental learning algorithm. In this method, the accumulated generating operation (AGO) with the raw time series is taken to improve the data quality and regularity firstly; then the prediction is conducted by a modified LS-SVR model, which simplifies the calculation process with incremental learning; finally, the inverse accumulated generating operation (IAGO) is performed to get the prediction results. The results of the prediction experiments indicate preliminarily that the proposed scheme is an effective prediction approach for its good prediction precision and less computing time. The method will be useful in actual application

Combining Canonical Variate Analysis, Probability Approach and Support Vector Regression for Failure Time Prediction

Reciprocating compressors are widely used in oil and gas industry for as transport, lift and injection. Critical reciprocating compressors that operate under high-speed conditions and compress hazardous gases are target equipment on maintenance improvement lists due to downtime risks and safety hazards. Estimating performance deterioration and failure time for reciprocating compressors could potentially reduce downtime and maintenance costs, and improve safety and availability. This study presents an application of Canonical Variate Analysis (CVA), Cox Proportional Hazard (CPHM) and Support Vector Regression (SVR) models to estimate failure degradation and remaining useful life based on sensory data acquired from an operational industrial reciprocating compressor. CVA was used to extract a one-dimensional health indicator from the multivariate data sets, thereby reducing the dimensionality of the original data matrix. The failure rate was obtained by using the CPHM based on historical failure times. Furthermore, a SVR model was used as a prognostic tool following training with failure rate vectors obtained from the CPHM and the one-dimensional performance measures obtained from the CVA model. The trained SVR model was then utilized to estimate the failure degradation rate and remaining useful life. The results indicate that the proposed method can be effectively used in real industrial processes to predict performance degradation and failure time

Multiple physical signals based residual life prediction model of slewing bearing

Critical failure of a slewing bearing used in large machines would entail high costs to an enterprise. Designing the condition monitoring system to diagnose the failure or predict the residual life of the slewing bearing is a practical and effective method to reduce unexpected stoppage or optimize the maintenances. Many literatures mentioned the life prediction of small typical rolling bearings based on the vibration signal. However slewing bearing is a large low-speed heavy-load bearing completely different from small bearing. Some researchers focused on the fault diagnosis of slewing bearing using non-traditional methods with vibration signals. And no published literatures mention the life prediction researches of slewing bearings based on the condition monitoring. Therefore, this paper presents a residual life model for slewing bearing based on multiple physical signals (torque, temperature and vibration). The correlation analysis and principal component analysis (PCA) based multiple sensitive features in time-domain were used to establish the performance recession indicators of temperature, torque and vibration, and these three indicators are input to the support vector regression (SVR) to construct the residual life model. The test results show that the PCA fusion combined correlation based features selection is an effective method of choosing the performance regression indicators, which is able to make full use of various features. The residual life prediction model based on temperature, torque and vibration signals can well reflect the performance recession trend and is suitable to predict the residual life of slewing bearing effectively

Circular domain features based condition monitoring for low speed slewing bearing

This paper presents a novel application of circular domain features calculation based condition monitoring method for low rotational speed slewing bearing. The method employs data reduction process using piecewise aggregate approximation (PAA) to detect frequency alteration in the bearing signal when the fault occurs. From the processed data, circular domain features such as circular mean, circular variance, circular skewness and circular kurtosis are calculated and monitored. It is shown that the slight changes of bearing condition during operation can be identified more clearly in circular domain analysis compared to time domain analysis and other advanced signal processing methods such as wavelet decomposition and empirical mode decomposition (EMD) allowing the engineer to better schedule the maintenance work. Four circular domain features were shown to consistently and clearly identify the onset (initiation) of fault from the peak feature value which is not clearly observable in time domain features. The application of the method is demonstrated with simulated data, laboratory slewing bearing data and industrial bearing data from Coal Bridge Reclaimer used in a local steel mill