Papers by Keyword: Health Assessment

Abstract: A hydraulic servo system is a typical feedback control system. Health assessment of a hydraulic servo system is usually difficult to realize when traditional methods based on sensor signals are utilized. An approach for health assessment of hydraulic servo systems based on multi-fractal analysis and Gaussian mixture model (GMM) is proposed in this study. A GRNN neural network is employed to establish a fault observer for the hydraulic servo system. The observer is utilized to simulate the system output under normal state. The residue is then generated by subtracting the estimated output from the actual output. The residue’s feature is extracted by fractal analysis. After the feature extraction, the overlap between the current feature vectors and the normal feature vectors is obtained by applying GMM. The confidence value (CV) can be obtained in advance; this value is employed to characterize the health degree of the current state and consequently implement the health assessment of the hydraulic servo system. Lastly, two common types of fault, namely, burst and gradual, are applied to validate the effectiveness of the proposed method.

Abstract: The aircraft environmental control system (ECS) is a critical aircraft system that provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have elicited an increasing amount of attention in recent years. The heat exchanger is a particularly significant component of ECS because its failure reduces the system’s efficiency and can lead to catastrophic consequences. Health assessment and fault diagnosis for the heat exchanger are necessary to perform maintenance and prevent risks in a timely manner. This paper presents fault-related parameter estimation methods based on strong tracking filter (STF) and logistic regression (LR) algorithm for heat exchanger health assessment and root cause classification, respectively. Heat exchanger fault simulation is conducted to generate performance degradation data, through which the proposed methods are validated. Results demonstrate that the proposed methods are capable of providing stable, effective, and accurate heat exchanger health assessment and root cause classification.

Abstract: Health assessment of the girder is crucial to an overhead traveling crane. This paper presents an intelligent damage identification method for the girder based on stiffness variation index (SVI) and least squares support vector machine (LSSVM). In the method, the SVI indicators, which have high resolution to environmental noise, serve as the damage feature to detect damage locations. Moreover, the SVI indicators are input to the LSSVM classifier for identifying the actual damage level of the girder. A case study on girder damage identification demonstrates that the method could determine the actual conditions of the girder structure accurately.

Abstract: An intelligent rolling bearing fault diagnosis method is proposed using empirical mode decomposition (EMD)–Teager energy operator (TEO) and Mahalanobis distance. EMD can adaptively decompose vibration signals into a series of intrinsic mode functions (IMFs), which are zero mean monocomponent AM–FM signals. TEO can estimate the total mechanical energy required to generate signals. Thus, TEO exhibits good time resolution and self-adaptive ability with regard to the transient components of the signal, which is an advantage in detecting signal impact characteristics. With regard to the impulse feature of the bearing fault vibration signals, TEO can be used to detect the cyclical impulse characteristic caused by bearing failure, gain an instantaneous amplitude spectrum for each IMF component, and then identify the characteristic frequency of a single, interesting IMF component in the bearing fault by means of the Teager energy spectrum. The amplitude of the Teager energy spectrum in the inner race and outer race fault frequencies, as well as the ratio of the energy of the resonance frequency band to the total energy, were extracted as feature vectors, which were then separately used as training samples and test samples for fault diagnosis. Thereafter, the Mahalanobis distances between the real measure and the different overall types of fault samples were calculated to classify the real condition of the rolling bearing. Finally, the Mahalanobis distances were converted into CV values, which assessed the current health state of the rolling bearing. Experimental results prove that this method can accurately identify and diagnose different fault types of rolling bearings.

Abstract: Many research papers implemented fault diagnosis and prognosis when there are many history data. However, for some capital and high reliability equipment, it is very difficult to acquire some run-to-failure data. In this case, the fault diagnosis and prognosis become very hard. In order to address this issue, continuous hidden Markov model (CHMM) is used to track the degradation process in this paper. With the degradation, the log-likelihood which is the output of CHMM will decrease gradually. Therefore, this indicator can be used to evaluate the health condition of monitored equipment. Finally, bearing run-to-failure data sets are used to validate the model’s effectiveness

Abstract: Health assessment is one of the key technology in the aircraft operating system. Aiming at the characteristic of aircraft structure, the aircraft fault prediction method based on data mining is presented in this paper. The concept of health assessment is introduced first, the wavelet neural network provide the mathematical model reflecting aircraft health state. The experiment results show that the health prediction applying wavelet neural network works well with high fidelity and real time. Focusing at a typical heavy-duty gas turbine, the critical information collected by the sensor is applied as the network input, then the wavelet neural network is constructed, the quick training and learning speed is proved. The results indicate proposed approach is promising for reliable diagnostics of aircraft.

Abstract: In order to determine the health status of Larix principis-rupprechtii plantation in the Eastern Inner Mongolia, the field data were surveyed and then the principal component analysis, mean-variance analysis method and fuzzy comprehensive evaluation method based on Fortran program were comprehensively applied to evaluate these forests in this study. Results showed that the health grade of Larix principis-rupprechtii plantation was 2.267. The health grade was between the sub-health and medium health, which was lean to the sub-health in the eastern Inner Mongolia.

Abstract: Increasing sophistication of naval shipboard machinery coupled with increasing competition for skilled manpower and tightening of defense budgets is forcing the Navy to consider alternative maintenance concepts. This paper proposes a health assessment process in condition-based maintenance architecture based on variable weight fuzzy comprehensive evaluation method. Through analyzing the variables affecting on warship electromechnical equipment condition, a layered assessment index system for warship electromechnical equipment health assessment is built. First, the AHP method is used to determine the constant weight of every index, and then the variable weight model is introduced based on balanced function, in which the constant weight is modified to get the variable weights. By comparing the fuzzy synthetic evaluation based on constant weights with that based on variable weighs, the results show that, the latter is more reasonable and objective, and is also more close to the actual condition of warship electromechnical equipment.

Abstract: Aiming at the high fault rate and the problem of high fault harmfulness of civil aircraft hydraulic pumping source system, the fuzzy synthetic evaluation algorithm is researched. Aiming at the complex system, more influenced factors, the analytic hierarchy process (AHP) is presented to establish the weight set. Aiming at the real-time need of health assessment, the expert system is used to build membership function. Finally the method is validated by a set of data of that system.

Abstract: Human health assessment is considered as a complex process with uncertainty and serious interconnection. It is impossible to establish a precise model for the process with traditional methods. According to the medical experience and the data from bioelectrical impedance analysis (BIA), an intelligent structure model is presented in this study. The assessment process can be divided into decision level and evaluation level, and each level is decomposed into 3 stages. In the process of assessment, the transferring manner is defined based on finite-state-machine and the Analytic Hierarchy Process is applied. The problem of interconnection and uncertainty in the system can be solved by the model and it brings about a method to deal with the problem of modelling complex system.