236 research outputs found

    Blade fault diagnosis using artificial intelligence technique

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    Blade fault diagnosis is conventionally based on interpretation of vibration spectrum and wavelet map. These methods are however found to be difficult and subjective as it requires visual interpretation of chart and wavelet color map. To overcome this problem, important features for blade fault diagnosis in a multi row of rotor blade system was selected to develop a novel blade fault diagnosis method based on artificial intelligence techniques to reduce subjective interpretation. Three artificial neural network models were developed to detect blade fault, classify the type of blade fault, and locate the blade fault location. An experimental study was conducted to simulate different types of blade faults involving blade rubbing, loss of blade part, and twisted blade. Vibration signals for all blade fault conditions were measured with a sampling rate of 5 kHz under steady-state conditions at a constant rotating speed. Continuous wavelet transform was used to analyse the vibration signals and its results were used subsequently for feature extraction. Statistical features were extracted from the continuous wavelet coefficients of the rotor operating frequency and its corresponding blade passing frequencies. The extracted statistical features were grouped into three different feature sets. In addition, two new feature sets were proposed: blade statistical curve area and blade statistical summation. The effectiveness of the five different feature sets for blade fault detection, classification, and localisation was investigated. Classification results showed that the statistical features extracted from the operating frequency to be more effective for blade fault detection, classification, and localisation than the statistical features from blade passing frequencies. Feature sets of blade statistical curve area was found to be more effective for blade fault classification, while feature sets of blade statistical summation were more effective for blade fault localisation. The application of feature selection using genetic algorithm showed good accuracy performance with fewer features achieved. The neural network developed for blade fault detection, classification, and localisation achieved accuracy of 100%, 98.15% and 83.47% respectively. With the developed blade fault diagnosis methods, manual interpretation solely dependent on knowledge and the experience of individuals can be reduced. The novel methods can therefore be used as an alternative method for blade fault diagnosis

    A Signal Processing Method for Hits Detection and Separation in High AE Activity Systems: Application to Composite Materials under Fatigue Tests.

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    International audienceThe threshold-based technique, employed by most of the commercial parameter-based AE systems for hit detection, uses a defined threshold in order to detect the start and the end of the hits. When dealing with continuous emission, the threshold-based technique is not suitable as the burst never drop below the threshold. In this case, the AE system is obliged to force the end of the hit after a defined maximum duration. This issue is encountered in complex systems such as in Organic Matrix Composites (OMC) fatigue tests where a high AE activity is observed, especially when the loading frequency overpasses few hertz. In this case, continuous signals can be generated by the background noise and rubbing, as well as by both cumulated damage (friction of crack surfaces) and damage growth. This paper deals with continuous AE signals obtained from experimental tests carried on Carbon Fiber Reinforced Plastics specimens. A numerical routine was implemented allowing the treatment of these signals. As the size of each acquisition is large due to the sampling rate (generally from 2 to 5 MS/s), the signal was divided into short segments. The Discrete Wavelet Transform (DWT) was used for signal denoising after adapting a certain number of parameters. Hit detection and determination was thereafter performed in order to localize potential hits contained in each signal segment. Conventional AE features were then calculated. By comparing the obtained results to those of conventional threshold-based techniques, we remark that the problem of erroneous hits is overcome. AE information that was hidden by the effect of the noise is now revealed allowing a further interpretation of damage mechanisms in the composite

    Blade faults diagnosis in multi stage rotor system by means of wavelet analysis

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    Blade fault is one of the most causes of gas turbine failures. Vibration spectral analysis and blade pass frequency (BPF) monitoring are the most widely used methods for blade fault diagnosis. These methods however have limitations in the detection of incipient faults due to weak and/or transient signals, as well as inability to diagnose the blade faults types. This study investigates the applications of wavelet analysis in blade fault diagnosis of a multi stage rotor system, as an extension of previous works which involved a single stage only. Results showed that conventional wavelet analysis has limitations in segregating the BPFs and locating the faults. An improvement in Morlet wavelet was made to achieve high resolution in both time and frequency domains. Two new wavelets for high time-frequency resolutions were formulated and added to the standard MATLAB Wavelet Toolbox. The optimal parameters for the high frequency resolution wavelet were found at the centre of frequency, ????=4 and bandwidth, ??=0.5. For high time resolution wavelet, the optimal parameters were ????=4 and ??=10. A novel algorithm was formulated by combining the two newly developed wavelets. A variety of blade faults including blade creep rubbing, blade tip rubbing, stage rubbing, blade loss of part and blade twisting were tested and their vibration responses measured in a laboratory test facility. The proposed method showed potential in segregating closely spaced BPFs components and identifying the faulty stage and fault location. The method demonstrated the ability in differentiating various blade faults based on a unique pattern (“fingerprint”) of each fault produced by the newly added wavelet. The formulated algorithm was demonstrated to be suitable in monitoring rotor systems with multiple blade stages

    Acoustic emission monitoring of propulsion systems : a laboratory study on a small gas turbine

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    The motivation of the work is to investigate a new, non-intrusive condition monitoring system for gas turbines with capabilities for earlier identification of any changes and the possibility of locating the source of the faults. This thesis documents experimental research conducted on a laboratory-scale gas turbine to assess the monitoring capabilities of Acoustic Emission (AE). In particular it focuses on understanding the AE behaviour of gas turbines under various normal and faulty running conditions. A series of tests was performed with the turbine running normally, either idling or with load. Two abnormal running configurations were also instrumented in which the impeller was either prevented from rotation or removed entirely. With the help of demodulated resonance analysis and an ANN it was possible to identify two types of AE; a background broadband source which is associated with gas flow and flow resistance, and a set of spectral frequency peaks which are associated with reverberation in the exhaust and coupling between the alternator and the turbine. A second series of experiments was carried out with an impeller which had been damaged by removal of the tips of some of the blades (two damaged blades and four damaged blades). The results show the potential capability of AE to identify gas turbine blade faults. The AE records showed two obvious indicators of blade faults, the first being that the energy in the AE signals becomes much higher and is distinctly periodic at higher speeds, and the second being the appearance of particular pulse patterns which can be characterized in the demodulated frequency domain

    Study and Application of Acoustic Emission Testing in Fault Diagnosis of Low-Speed Heavy-Duty Gears

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    Most present studies on the acoustic emission signals of rotating machinery are experiment-oriented, while few of them involve on-spot applications. In this study, a method of redundant second generation wavelet transform based on the principle of interpolated subdivision was developed. With this method, subdivision was not needed during the decomposition. The lengths of approximation signals and detail signals were the same as those of original ones, so the data volume was twice that of original signals; besides, the data redundancy characteristic also guaranteed the excellent analysis effect of the method. The analysis of the acoustic emission data from the faults of on-spot low-speed heavy-duty gears validated the redundant second generation wavelet transform in the processing and denoising of acoustic emission signals. Furthermore, the analysis illustrated that the acoustic emission testing could be used in the fault diagnosis of on-spot low-speed heavy-duty gears and could be a significant supplement to vibration testing diagnosis

    Review of recent advances in the application of the wavelet transform to diagnose cracked rotors

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    Wavelet transform (WT) has been used in the diagnosis of cracked rotors since the 1990s. At present, WT is one of the most commonly used tools to treat signals in several fields. Understandably, this has been an area of extensive scientific research, which is why this paper aims to summarize briefly the major advances in the field since 2008. The present review considers advances in the use and application of WT, the selection of the parameters used, and the key achievements in using WT for crack diagnosis.The authors would like to thank the Spanish government for financing through the CDTI project RANKINE21 IDI-20101560

    Novel complete ensemble EMD with adaptive noise-based hybrid filtering for rolling bearing fault diagnosis

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    A feature extraction of fault bearing has attracted considerable attention in recent years. However, weak fault feature is difficult to extract under heavy background noise. To solve this problem, a novel multi-layer filtering method is proposed to filter out noise and extract weak fault feature. The first layer introduces a metric based on de-trended fluctuation analysis (DFA) to identify intrinsic mode function (IMF) that reflect period impulsive information for vibration signal adaptively. The second layer uses non-local mean (NLM) method as a pre-filter of the third layer to realize extraction of singular value decomposition (SVD) which reflect the most information of IMFs. The last layer introduces a relative energy difference criterion of a singular value to extract important feature of Hankel matrix of IMFs. The filtered signal is obtained by re-constructed signal from identified singular value of SVD. Experiment results on simulation and real vibration signals indicate that the hybrid filtering method removes heavy noise successfully and extract weak fault feature of rolling bearing effectively

    Combined fault detection and classification of internal combustion engine using neural network

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    Different faults in internal combustion engines leads to excessive fuel consumption, pollution, acoustic emission and wear of engine components. Detection of fault is also difficult for maintenance technicians due to broad range of faults and combination of the faults. In this research the faults due to malfunction of manifold absolute pressure, knock sensor and misfire are detected and classified by analyzing vibration signals. The vibration signals acquired from engine block were preprocessed by wavelet analysis, and signal energy is considered as a distinguishing property to classify these faults by a Multi-Layer Perceptron Neural Network (MLPNN). The designed MLPNN can classify these faults with almost 100 % efficiency

    A Diagnosis Feature Space for Condition Monitoring and Fault Diagnosis of Ball Bearings

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    The problem of fault diagnosis and condition monitoring of ball bearings is a multidisciplinary subject. It involves research subjects from diverse disciplines of mechanical engineering, electrical engineering and in particular signal processing. In the first step, one should identify the correct method of investigation. The methods of investigation for condition monitoring of ball bearings include acoustic emission measurements, temperature monitoring, electrical current monitoring, debris analysis and vibration signal analysis. In this thesis the vibration signal analysis is employed. Once the method of analysis is selected, then features sensitive to faults should be calculated from the signal. While some of the features may be useful for condition monitoring, some of the calculated features might be extra and may not be helpful. Therefore, a feature reduction module should be employed. Initially, six features are selected as a candidate for the diagnosis feature space. After analyzing the trend of the features, it was concluded that three of the features are not appropriate for fault diagnosis. In this thesis, two problem is investigated. First the problem of identifying the effects of the fault size on the vibration signal is investigated. Also the performance of the feature space is tested in distinguishing the healthy ball bearings from the defective vibration signals
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