Adaptive prognosis of centrifugal pump under variable operating conditions
| Autor: | Jinjiang Wang, Laibin Zhang, Kebo Wang, Yinghao Zheng |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Multivariate statistics Downtime Computer science Mechanical Engineering Aerospace Engineering Condition monitoring Multivariate normal distribution 02 engineering and technology Centrifugal pump Mixture model 01 natural sciences Computer Science Applications 020901 industrial engineering & automation Control and Systems Engineering Control theory 0103 physical sciences Signal Processing Particle filter 010301 acoustics Reliability (statistics) Civil and Structural Engineering |
| Zdroj: | Mechanical Systems and Signal Processing. 131:576-591 |
| ISSN: | 0888-3270 |
| DOI: | 10.1016/j.ymssp.2019.06.008 |
| Popis: | Condition monitoring and prognosis of a centrifugal pump is essential to increase its reliability and safety, and reduce downtime and maintenance costs. Due to the complexity and variety of its operating conditions in practice, the distribution characteristics of data collected from a centrifugal pump shows complex multivariate distributions. On the other hand, the machinery degradation process is also characterized by stochasticity and nonlinearity. To address these challenges, this paper presents an adaptive prognosis method incorporating operating condition identification and Bayesian prediction in one framework. Specifically, a multivariate distribution based unsupervised clustering method is presented to identify operating conditions of pumps based on Gaussian mixture model and Expectation-Maximization algorithm. Then the future machinery status is predicted for defect severity analysis and remaining useful life estimation under the identified operating conditions based on particle filter method. The uncertainty of the prediction results is also quantified and updated with the instrumented measurements. An experimental study on an oil centrifugal pump in the field is performed to demonstrate the effectiveness of the presented adaptive prognosis method over conventional prediction approaches. |
| Databáze: | OpenAIRE |
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