Recent Progress on Mechanical Condition Monitoring and Fault diagnosis
Abstract: Mechanical equipments are widely ud in various industrial applications. Generally working in vere conditions, mechanical equipments are subjected to progressive deterioration of their state. The mechanical failures account for more than 60% of breakdowns of the system. Therefore, the identification of impending mechanical fault is crucial to prevent the system from malfunction. This paper discuss the most recent progress in the mechanical condition monitoring and fault diagnosis. Excellent work is introduced from the aspects of the fault mechanism rearch, signal processing and feature extraction, fault reasoning rearch and equipment development. An overview of some of the existing methods for signal processing and feature extraction is prented. The advantages and disadvantages of the techniques are discusd. The review result suggests that the intelligent information fusion bad mechanical fault diagnosis expert system with lf-learning and lf-updating abilities is the future rearch trend for the condition monitoring fault diagnosis of mechanical equipments.斑怎么组词
Keywords: Condition monitoring; Fault diagnosis; Vibration; Signal processing
1. Introduction
With the development of modern science and technology, machinery and equipment functions are becoming more and more perfect, and the machinery structure becomes more large-scale, integrated, intelligent and complicated. As a result, the component number increas significantly and the precision requirement for the part mating is stricter. The possibility and category of the related component failures therefore increa greatly. Malignant accidents caud by component faults occur frequently all over the world, and even a small mechanical fault may lead to rious conquences. Hence, efficient incipient fault detection and diagnosis are critical to machinery normal running. Although optimization techniques have been carried out in the machine design procedure and the manufacturing procedure to improve the quality of mechanical products, mechanical failures are still difficult to avoid due to the complexity of modern equipments. The condition monitoring and fault diagnosis bad on advanced science and technology acts as an efficient mean to forecast potential faults and reduce the cost of machine malfunctions. This is the so-called mechanical equipment fault diagnosis technology eme
张国荣的歌曲rged in the nearly three decades [1, 2].
Mechanical equipment fault diagnosis technology us the measurements of the monitored machinery in operation and stationary to analyze and extract important characteristics to calibrate the states of the key components. By combining the history data, it can recognize the current conditions of the key components quantitatively, predicts the impending abnormalities and faults, and prognos their future condition trends. By doing so, the optimized maintenance strategies can be ttled, and thus the industrials can benefit from the condition maintenance significantly [3, 4].
The contents of mechanical fault diagnosis contain four aspects, including fault mechanism rearch, signal processing and feature extraction, fault reasoning rearch and equipment development for condition monitoring and fault diagnosis. In the past decades, there has been considerable work done in this general area by many rearchers. A conci review of the rearch in this area has been prented by [5, 6]. Some landmarks are discusd in this paper. The novel signal processing techniques are
prented. The advantages and disadvantages of the new signal processing and feature extraction methods are discusd in this work. Then the fault reasoning rearch and the diagnostic equipments are briefly reviewed. Finally, the future rearch topics are described in the point of future generation intelligent fault diagnosis and prognosis system.
火车票退票新规定2. Fault Mechanism Rearch
打扫卫生的英语 Fault Mechanism rearch is a very difficult and important basic project of fault diagnosis, same as the pathology rearch of medical. American scholar John Sohre, published a paper on "Caus and treatment of high-speed turbo machinery operating problems (failure)", in the United States Institute of Mechanical Engineering at the Petroleum Mechanical Engineering in 1968, and gave a clear and conci description of the typical symptoms and possible caus of mechanical failure. He suggested that typical failures could be classified into 9 types and 37 kinds [7]. Following, Shiraki [8] conduced considerable work on the fault mechanism rearch in Japan during 60s-70s la
st century, and concluded abundant on-site troubleshooting experience to support the fault mechanism theory. BENTLY NEVADA Corporation has also carried out a ries experiments to study the fault mechanism of the rotor-bearing system [9]. A large amount of related work has been done in China as well. Gao et al. [10] rearched the vibration fault mechanism of the high-speed turbo machinery, investigated the relationship between the vibration frequency and vibration generation, and drew up the table of the vibration fault reasons, mechanism and recognition features for subsynchronous, synchronous and super-synchronous vibrations. Bad on the table they propod, they have classified the typical failures into 10 types and 58 kinds, and provided preventive treatments during the machine design and manufacture, Installation and maintenance, operation, and machine degradation. Xu et al. [11] concluded the common faults of the rotational machines. Chen et al. [12] ud the nonlinear dynamics theory to analyze the key vibration problems of the generator shaft. They established a rotor nonlinear dynamic model for the generator to comprehensively investigate the rotor dynamic behavior under various influences, and propod an effective solution to prevent rotor failures. Yang et al.
[13] adopted vibration analysis to study the fault mechanism of a ries of diel engines. Other rearchers have done a lot in the fault mechanism of mechanics since 1980s, and have published many valuable papers to provide theory and technology supports in the application of fault diagnosis systems [14-18]. However, most of the fault mechanism rearch is on the qualitative and numerical simulation stage, the engineering practice is difficult to implement. In addition, the fault information often prents strong nonlinear, non stationary and non Gaussian characteristics, the simulation tests can not reflect the characteristics very accurately. The fault diagnosis results and the application possibility may be influenced significantly. As a result, the development of the fault diagnosis technique still faces great difficulties.
3. Advanced Signal Processing and Feature Extraction Methods
Advanced signal processing technology is ud to extract the features which are nsitive to specific fault by using various signal analysis techniques to process the measured signals. Condition information of the plants is contained in a wide range of sign
als, such as vibration, noi, temperature, pressure, strain, current, voltage, etc. The feature information of a certain fault can be acquired through signal analysis method, and then fault diagnosis can be done correspondingly. To meet the specific needs of fault diagnosis, fault feature extraction and analysis technology is undergoing the process from time domain analysis to Fourier analysis-bad frequency-domain analysis, from linear stationary signal analysis to nonlinear and nonstationary analysis, from frequency-domain analysis to time-frequency analysis. Early rearch on vibration signal analysis is mainly focud on classical signal analysis which made a lot of rearch and application progress. Rotating mechanical vibration is usually of strong harmonic, its fault is also usually registered as changes in some harmonic components. Classical spectrum analysis bad on Fourier transform (such as average time-domain techniques, spectrum analysis, cepstrum analysis and demodulation techniques) can extract the fault characteristic information effectively, thus it is widely ud in motive power machine, especially in rotating machinery vibration monitoring and fault diagnosis. In a manner of speaking, classical signal analysis is still the main method for mechanical疫情教案 vibration signal
analysis and fault feature extraction. However, classical spectrum analysis also has垃圾分类海报 obvious disadvantages. Fourier transform reflects the overall statistical properties of a signal, and is suitable for stationary signal analysis. In reality, the signals measured from mechanical equipment are ever-changing, non-stationary, non-Gaussian distribution and nonlinear random. Especially when the equipment breaks down, this situation appears to be more prominent. For non-stationary signal, some time-frequency details can not be reflected in the spectrum and its frequency resolution is limited using Fourier transform. New methods need to be propod for tho nonlinearity and non-stationary signals.冬夜读书示子聿古诗 The strong demand from the engineering practice also contributes to the rapid development of signal 闻香师analysis. New analytical methods for non-stationary signal and nonlinear signal are emerging constantly, which are soon applied in the field of machinery fault diagnosis. New methods of signal analysis are main including time-frequency analysis, wavelet analysis, Hilbert-Huang transform, independent component analysis, advanced statistical analysis, nonlinear signal analysis and so on. The advantages and disadvantages of the approaches are discusd below.
