Biqing Wu

A Particle Filtering Framework for Failure Prognosis

Bayesian estimation techniques are finding application domains in machinery fault diagnosis and prognosis of the remaining useful life of a failing component/subsystem. This paper introduces a methodology for accurate and precise prediction of a failing component based on particle filtering and learning strategies. This novel approach employs a state dynamic model and a measurement model to predict the posterior probability density function of the state, i.e., to predict the time evolution of a fault or fatigue damage. It avoids the linearity and Gaussian noise assumption of Kalman filtering and provides a robust framework for long-term prognosis while accounting effectively for uncertainties. Correction terms are estimated in a learning paradigm to improve the accuracy and precision of the algorithm for long-term prediction. The proposed approach is applied to a crack fault and the results support its robustness and superiority.Copyright

Integrated diagnosis and prognosis architecture for fleet vehicles using dynamic case-based reasoning

This paper presents a hybrid reasoning architecture for integrated fault diagnosis and health maintenance of fleet vehicles. The aim of this architecture is to research, develop and test advanced diagnostic and decision support tools for maintenance of complex machinery. Artificial intelligence based diagnostic approach has been proposed with particular reference to dynamic case-based reasoning (DCBR). This system refines an asynchronous stream of symptom and repair actions into a compound case structure and efficiently organizes the relevant information into the case memory. Diagnosis is carried out into two steps for fast and efficient solution generation. First the situation is analyzed based on observed symptoms (textual descriptions) to propose initial diagnosis and generate corresponding explanation hypothesis. Next, based on the generated hypothesis relevant sensor data is collected and corresponding data analysis modules are activated for data-driven diagnosis. This approach reduces the computational demands to enable fast experience transfer and more reliable and informed testing. This system also tracks the success rates of all possible hypotheses for a given diagnosis and ranks them based on statistical evaluation criteria to improve the efficiency of future situations. Since the system can interact with multiple vehicles it learns about several operating environments resulting in a rich accumulation of experiences in relatively very short time. A distributed and generic architecture of this system is outlined from technical implementation point of view which can be used for widespread applications where both qualitative and quantitative observations can be gathered. Further, a concept of expanding this architecture for carrying out prognostic tasks is introduced.

A methodology for analyzing vibration data from planetary gear systems using complex Morlet wavelets

Planetary gear trains are complex flight critical components of helicopters and other aircraft. Failure modes on such components may lead to loss of life and/or aircraft. It is essential, therefore, that incipient failures or faults be detected and isolated as early as possible and corrective action be taken in order to avoid catastrophic events. Research thus far has focused on gear teeth faults and available methods could not detect a crack in the planetary gear plate under all operating conditions. A wavelet domain methodology is suggested for the analysis and feature extraction of the vibration data from the planetary gear system of military helicopters. Complex Morlet wavelets are employed and the time domain knowledge, preserved by the wavelet decomposition, is used to extract useful features that distinguish between faulted and healthy gear plates from experimental data made available from both on-aircraft and test cell experiments. A statistical method based on the z-test is also suggested to evaluate the relative performance of these features.

A hybrid reasoning architecture for fleet vehicle maintenance

This article has described a novel approach for integrated diagnosis/prognosis of systems. The suggested architecture enables encoding of analytical techniques from a systems point of view and its expansion for prognosis tasks under the same structure. The performance of such a knowledge-based system depends on the degree of completeness of its enables encoding of analytical techniques from a systems point of view and its expansion for prognosis tasks under the same structure. The performance of such a knowledge-based system depends on the degree of completeness of its knowledge base. Since the system can interact with multiple vehicles, it learns about several operating environments, resulting in a rich accumulation of experiences in relatively very short time. At the same time, it also serves multiple systems. A natural language processing technique has been developed to extract information from the textual descriptions that is less computationally expensive than the usual NLP techniques and still preserves the meaning of the text. The experimental test data are currently being gathered for the experiments from the domain of automobiles to demonstrate the capability of the system

VIBRATION MONITORING FOR FAULT DIAGNOSIS OF HELICOPTER PLANETRY GEARS

Abstract In this paper, vibration data analysis techniques are investigated for fault diagnosis of helicopter planetary gears. A data pre-processing technique is introduced that achieves the same result as the commonly used Time Synchronous Averaging with much lower computational complexity since interpolation is not required. A notion of using raw vibration data instead of the Time Synchronous Averaged data is also presented that is more suitable for the analysis of vibration data produced by planetary gearboxes and for the purposes of detecting carrier plate crack fault. Based on this notion, features such as the Harmonic Index in the frequency domain and the Intra-Revolution Energy Variance in the wavelet domain are derived. The features are used as inputs to fault classifiers and are shown to detect the fault successfully based on the test data that is available.