Aisong Qin

Concurrent Fault Diagnosis for Rotating Machinery Based on Vibration Sensors

Rotating machinery is widely used in modern industry. It is one of the most critical components in a variety of machinery and equipment. Along with the continuous development of science and technology, the structures of rotating machinery become of larger scale, of higher speed, and more complicated, which results in higher probability of concurrent failure in practice. It is important to enable reliable, safe, and efficient operation of large-scale and critical rotating machinery, which requires us to achieve accurate diagnosis of concurrent fault, for example, rolling bearing diagnosis, gearbox diagnosis, and compressor diagnosis. In this paper, to achieve concurrent fault diagnosis for rotating machinery, which cannot be accurately diagnosed by existing methods, we develop an integrated method using artificial immune algorithm and evidential theory.

Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator

Remaining useful life (RUL) prediction can provide early warnings of failure and has become a key component in the prognostics and health management of systems. Among the existing methods for RUL prediction, the Wiener-process-based method has attracted great attention owing to its favorable properties and flexibility in degradation modeling. However, shortcomings exist in methods of this type; for example, the degradation indicator and the first predicting time (FPT) are selected subjectively, which reduces the prediction accuracy. Toward this end, this paper proposes a new approach for predicting the RUL of rotating machinery based on an optimal degradation indictor. First, a genetic programming algorithm is proposed to construct an optimal degradation indicator using the concept of FPT. Then, a Wiener model based on the obtained optimal degradation indicator is proposed, in which the sensitivities of the dimensionless parameters are utilized to determine the FPT. Finally, the expectation of the predicted RUL is calculated based on the proposed model, and the estimated mean degradation path is explicitly derived. To demonstrate the validity of this model, several experiments on RUL prediction are conducted on rotating machinery. The experimental results indicate that the method can effectively improve the accuracy of RUL prediction.

Vibration sensor based intelligent fault diagnosis system for large machine unit in petrochemical industries

Fault diagnosis is an area which is gaining increasing importance in rotating machinery. Along with the continuous advance of science and technology, the structures of rotating machinery become increasingly of larger scale and higher speed and more complicated, which result in higher probability of various failure in practice. In case one of the most critical components of machinery or equipment breaks down, it cannot only cause enormous economic loss, but also easily cause the loss of many peoples lives. It is important to enable reliable, safe, and efficient operation of large-scale and critical rotating machinery, which requires us to achieve accurate and fast diagnosis of fault which has occurred. Aiming at dynamic real-time vibration monitoring and vibration signal analysis for large machine unit in petrochemical industry, which cannot realize real-time, online, and fast fault diagnosis, an intelligent fault diagnosis system using artificial immune algorithm and dimensionless parameters is developed in this paper, innovated with a focus on reliability, remote monitoring, and practicality and applied to the third catalytic flue gas turbine in a petrochemical enterprise, with good effects.

Application of an information fusion method to compound fault diagnosis of rotating machinery

Aiming at how to use the multiple fault features information synthetically to improve accuracy of compound fault diagnosis, an information fusion method based on the weighted evidence theory was proposed to effectively diagnose compound faults of rotating machinery. Firstly multiple fault features were extracted by the genetic programming. Each fault feature was separately used to act as evidence and the initial diagnosis accuracy was regarded as the weight coefficient of the evidence. Then through the negative selection algorithm, the diagnosis ability of the local diagnosis was advanced and an impersonal means of obtaining basic probability assignment was given. Finally the fusion result was obtained by utilizing the weighted evidence theory into the decision-making information fusion for the preliminary result. By comparing the diagnosis results with other artificial intelligence algorithm, experiment result indicates that using multiple weighted evidences fusion can improve the diagnostic accuracy of compound fault.

A compound fault integrated diagnosis method for rotating machinery base on dimensionless immune detector

Real-time and accuracy fault diagnosis is the key technique to realize timely effective maintenance and health management for rotating machinery, most faults of which are compound under actual working conditions. Many compound faults are coupled with each other, fuzzy, object and some complex characteristics so it is a bottleneck problem which is very tough to break through in fault diagnosis field. Early research results indicate that diagnosis with dimensionless parameters have got good effects in single fault for rotating machinery, but under simulating actual working state of compound faults, there are obvious overlap in ranges of dimensionless parameters calculated by vibration monitoring data of each compound faults, that is to say, it is hard to distinguish the ranges of dimensionless parameters of each fault, causing complexity of diagnosis rise, and the existing method is hard to deal with this problem. To solve the difficult problem, an online fusion fault diagnosis method for rotating machinery based on dimensionless immune detector and evidence reasoning (ER) is proposed. Experimental result demonstrates that the method can realize effectively real-time fault diagnose for rotating machinery and has high potential applications in real project.

Fault diagnosis for rotating machinery based on artificial immune algorithm and evidence theory

Along with the continuous development of science and technology, the structures of rotating machinery become to be larger scale and more complicated, which results in higher probability of concurrent fault under actual working conditions. In order to achieve concurrent fault diagnosis for rotating machinery, an integrated method using artificial immune algorithm and evidence theory is proposed in this research work. The self-nonself recognition mechanism of artificial immune system for data analysis and processing has been derived from the negative selection algorithm. Five kinds of dimensionless immune detectors are generated based on negative selection algorithm, then the local diagnosis result of dimensionless immune detector is gotten. Combining with evidence theory fusion rules, the final diagnosis can be obtained. Experimental result demonstrates that the method can realize effectively concurrent fault diagnosis for rotating machinery.

Work in progress: Multi-dimensionless parameters fusion method based on improved D-S evidence theory

Aiming at the invalidation problem of Dempster-Shafer evidence combination rule with high conflict, a fault diagnosis method of multi-dimensionless parameters information fusion based on novel D-S algorithm improved by the weighted concept is proposed. Firstly, prior weights are obtained by the basis of inherent reliability of multi-dimensionless parameters diagnosis information, and a similarity matrix between evidences is constructed as posterior weights. Then the prior weights and posterior weights are combined regarded as the weight coefficient of the evidence and evidences are weighted with these weight coefficients. Finally the D-S evidence theory is used to fuse the weighted evidences and the final diagnosis result can be obtained. The experimental results demonstrated that, compared with other methods, the new algorithm can improve accuracy of fault diagnosis and reduce influence of conflict evidence effectively.

Vibration sensor based intelligent fault diagnosis system for large machine unit in petrochemical industry

In this paper, to satisfy the need of fault monitoring, dynamic real time vibration monitoring and vibration signal analysis for large machine unit in petrochemical industry, which cannot realize real-time, online and fast fault diagnosis, an intelligent fault diagnosis system is developed using artificial immune algorithm and dimensionless indicators, innovated with a focus on reliability, remote monitoring and practicality, and be applied to the Third Catalytic Flue Gas Turbine in a petrochemical enterprise and have got good effects.

New dimensionless parameter construction using genetic programming for fault classifying of rotating machinery

In this paper, an approach to apply genetic programming in combination with dimensionless parameter of time domain is proposed. According to this approach, new dimensionless parameter is constructed. The effectiveness of the new dimensionless parameter is validated by an illustrative experiment. Experimental result shows that the classification ability of new dimensionless parameter is better than that of existing ones.

The build of a new non-dimensional indicator for fault diagnosis in rotating machinery

In order to ensure the safety in petrochemical production, it is of great importance for key equipment in petrochemical production unit to maintain, monitor and diagnose fault. Non-dimensional indicator is insensitive to the change of working condition so it is applicable to fault diagnosis technology, yet it is not satisfactory for complex concurrent fault diagnosis in rotating machinery and the number of conventional non-dimensional indicators is small. Building new non-dimensional indicator, which especially possesses the features of integrated diagnosis, is very significant to improve capability to diagnose fault. In the paper, the method of building new non-dimensional indicator is presented. Based on the previous research findings about 3MGCA to search arithmetical combination of conventional non-dimensional indicator, new non-dimensional indicator is acquired using optimisation algorithm of measured fault data. Experiments show that the proposed method is practical and efficient for building new non-dimensional indicator in rotating machinery, and it has good potential application in the engineering practice.