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In sonic infrared imaging, the engagement force strongly affects the plate׳s vibration characteristics and frictional heating at the crack, which have been proved by experimental results. We developed a FEM model in which piezoelectric-force analogy was employed to simulate inverse piezoelectric effect. Simulation results revealed that an increase in the engagement force not only enhances the vibration intensity of the test plate but also causes a sharp change in its spatial distribution. However, when the increase exceeds a certain level, the enhanced crack closure and weakened crack differential velocity will lead to a lower heat dissipating at the crack.

Engagement force between ultrasonic horn and target is one of the primary factors which influence defect detection in sonic infrared imaging method. In this paper, an experimental setup which can quantify the engagement force has been designed. A series of experiments under different engagement forces have been performed, and an interesting phenomenon is found that the increase in engagement force enhances superharmonics and diminishes subharmonics in vibration velocity spectrum of the target. A corresponding mechanical model is presented to explain the mechanism of such phenomenon. Frequency-domain analysis on theoretical simulation further confirms the experimental results. Meanwhile, time-domain analysis on theoretical simulation demonstrates the variation of vibration velocity spectrum caused by engagement force. It is clear that the increasing engagement force can shorten the impact interval and prolong associated movement between the horn and target. And the relationship between engagement force and subharmonic-order can be described theoretically using an approximately hyperbolic curve.

Measuring the clamp-on pressure of high pressure fuel pipe is a shortcut for obtaining injection parameters and fault diagnosis of the fuel injection pumps [1,. Based on the analysis of the fuel injection process of fuel injection pump, analyzed the differences of practical injection process and geometric injection process, and obtained the theory waveform feature points of diesel engine high pressure pipe fuel injection process. In order to obtain the clamp-on pressure waveform which feature points consistent with the theoretical waveform, designed the hardware filter based on MAX293. Considering the clamp-on pressure signal sometimes must also meet the requirements of no delay, designed frequency domain filter. Two filtering methods have reached the expected results.

Ultrasonic infrared thermography is a novel nondestructive detection technique, which combines a short ultrasonic pulse excitation and infrared imaging to detect defects, such as crack, in materials and structures. A simplified one-dimension heat-conduction model excited by ultrasonic pulses is put forward in this paper. Based on this model, a serial of image processing methods for recognition and reconstruction of cracks were presented. Results obtained show that the proposed method is creditable and applicable.

Ultrasonic Infrared Imaging is a novel NDE technique, which performs well on material internal defect detection, such as metal fatigue crack, composite material impact damage and adhesion and so on. Traditional defect identification often depends on eyes and professional experience, which can’t give a clear conclusion of defect information. The identification algorithm based on time sequence images is low-level. Therefore, taking the crack detection in Ultrasonic IR Imaging as an example, after contrastive analysis of shape characters and gray distribution between crack region and normal region, characteristic parameters for different regions was creatively extracted in this paper. An automatic recognition algorithm based on Weighted Support Vector Machines is put forward for crack recognition. Subsequently, the correctness of the algorithm was validated by experiments.

Certain mechanical equipment control system of sampling signal processing in the digital content are discussed. Based on the wavelet transformation and multiresolution wavelet decomposition mathematics theory, the application of wavelet transform on feature extraction and noise removing; Instance are given.

The application based on Backpropagation (BP) Algorithm network is conducted on identifying the categories and numbers of mechanical equipments by acoustic signal in battlefield targets. Collected signal was pre-processed and extracted the power spectrum feature of acoustic signal as input vectors of neural networks, then classified by neural networks and pattern recognition theorem. We employ the acoustic signals of six kinds of normal equipments as training samples to train the network. The experiment shows that the ratio of recognition of the acoustic signal processing system based on neural networks proposed is better than the conventional methods.

Combining Self-organizing Feature Map (SOM) and Factorial hidden Markov model (FHMM), a new FHMM fault recognition method based on multi-sensor vibration information fusion is proposed. In the proposed method, the SOM neural network is used to reduce the information redundancy in feature vectors extracted from the multi-sensor’s vibration measurements, FHMM as a classifier. The fault recognition in the speed-up and speed-down process of rotating machinery was successfully completed. The experiment result shows that the proposed method is very effective.

Reliability of equipment has long been considered as an important quality characteristic. But the reliability assessment of multi-parameter system still remains a problem. Based on the multi-parameter degradation process of a certain diesel engine, the article proposes a reliability assessing method for multi-parameter degradation system. The method uses SVDD to convert multi-parameter into one single degradation measure which reveals the overall system degradation. Using degradation measure distribution, a reliability assessment model is built. Through experiments on vehicle, the method shows the degradation regular of vehicle diesel engine, builds the reliability curve, provides evidences for equipment maintenance.

This paper presents a reliability assessment method for the system with multiple degradation parameters based on SVDD and SVR. The method transforms different performance parameters of the system to a distance measure by SVDD, uses SVR to analyze and predict the distribution parameters of SVDD distance, then create the overall system performance degradation assessment model. Through vehicle tracking experiment, collecting different degradation parameters of the diesel engine, a reliability curve stands for the whole system degradation is achieved. Compared to traditional reliability assessment method, the new method combines the various parameters so that more effectively describes the diesel engine performance degradation, and is more instructive for the maintenance of the vehicle.

In the process of computed order tracking, traditional method is based on the given analyzed order requirements, and also assumed that the highest order component is known. But in practice, the order component in signal is usually unknown. With regards to this issue, the paper proposed a method using filter to limit frequency and order. A principle on how to choose filter cut-off frequency and angular domain resampling order are deduced in this paper, then the relationship is analyzed between filter cut-off frequency and resampling frequency in time domain. Simulated signals are tried on the method, the results show that it can solve order aliasing problem successfully, and also effectively reduce resampling order.

Reliability of equipment has long been considered as an important quality characteristic. Traditional methods of equipment reliability assessment are based on lifetime data. With equipment being much more reliable and the growing need for developing new equipments within shorter period and at lower cost, we can hardly get enough lifetime data in many cases. Performance degradation data can also be used for reliability assessment. This paper is mainly researched on reliability of a certain diesel engine. Through analyzing the failure mode and failure mechanism, performance degradation parameters of the diesel engine is selected as compression pressure in a cylinder while driven by an electrical motor. Based on a long time accumulation on performance degradation data, two methods of degradation path fitting and degradation value distribution are tried in this paper to evaluate reliability of the diesel engine. Besides, the regulation of the reliability variation is given by the results of the assessment.

A genetic algorithm-support vector regression model (GA-SVR) is proposed for machine performance degradation prediction. The main idea of the method is firstly to select the condition-sensitive features extracted from rolling bearing vibration signals using Genetic Algorithm to form a condition vector. Then prediction model is established for each feature time series. And the third step is to establish support vector regression models to obtain prediction result in each series. Finally, the condition prognosis can be obtained through combing all components to form a condition vector. Vibration data from a rolling bearing bench test process are used to verify accuracy of the proposed method. The results show that the model is an effective prediction method with a higher speed and a better accuracy.

This paper presents an approach by combining the genetic algorithm (GA) with simulated annealing (SA) algorithm for enhancing finite element (FE) model updating. The proposed algorithm has been applied to two typical rotor shafts to test the superiority of the technique. It also gives a detailed comparison of the natural frequencies and frequency response functions (FRFs) obtained from experimental modal testing, the initial FE model and FE models updated by GA, SA, and combination of GA and SA (GA–SA). The results concluded that the GA, SA, and GA–SA are powerful optimization techniques which can be successfully applied to FE model updating, but the appropriate choice of the updating parameters and objective function is of great importance in the iterative process. Generally, the natural frequencies and FRFs obtained from FE model updated by GA–SA show the best agreement with experiments than those obtained from the initial FE model and FE models updated by GA and SA independently.

The sonic infrared imaging technology is a new nondestructive detection technology combining ultrasonic excitation and infrared imaging which can detect the defects of structure. In this paper, a ferruginous plate is considered as the research object to open out mechanism of crack heating in this technology. A finite element model based on the transducer and plate is established, heating characteristics in the crack are studied here under different excitation time and different structural parameters, such as crack shape and location. Research results show that temperature in the crack area increase with the excitation time, but increasing rate becomes more and more small. And the influence of crack shape on crack heating presenting a monotonous function situation, while heating of cracks at different location demonstrates a phenomenon of rise and fall. The research of structural parameters on crack heating will establish the theory basis for mechanism research on crack heating in the sonic infrared imaging technology. Keywords-sonic infrared; structural parameter; finite element modeling; crack heating; nondestructive detection

From data collected by an online condition monitoring and fault diagnosis system, a higher pad temperature at the upper guide bearing in a pumped storage power generator unit installed in Guangdong province(GPSPS), China, was found. And also a relatively intensive shaft vibration occurred at the lower guide bearing. By calculating the Reynolds equation and viscosity-temperature equation of the lubricant, a curve between the pre-load coefficient and the increment of pad temperature is obtained, which shows that the larger, the pre-load coefficient, the bigger, the increment of pad temperature. For a practical unit in GPSPS, by employing Transfer matrix method and Wilson-θ method to analyze shaft vibration at different pre-load coefficients of the whole bearing or pad pair bearings, the results show that the larger the pre-load coefficient is, the smaller the vibration amplitude is, the shorter the time for vibration to become steady is. And an uneven pre-load coefficient of the pad pair bearings will cause shaft orbit from a circle to an ellipse whose long axes is at the direction of the pad pair with the lowest pre-load coefficient. Finally, reasons of higher pad temperature of the upper guide bearing and larger shaft vibration at the lower guide bearing are due to the inconsistent relation of bearing assembling clearance or pre-load coefficient of the upper and lower guide bearing, and also due to the too small, uneven pre-load coefficient of pad pair bearings. After a scheme for adjusting the bearing clearance is given, data measured show that the analysis and simulation methods are correct and the adjustment scheme to the assembling clearance of the upper and lower guide bearings is feasible and can be used to guide the field maintenance conveniently.

In order to reduce the influences caused by background noise and interference and exact the fault characteristic frequency, the basic theory of second-order cyclic statistics is studied in this chapter. The excellent demodulation ability of second-order cyclic statistics is proved by simulative analysis. In the bench test, the fault characteristics frequency of bearing outer raceway and its harmonic can be recognized clearly in frequency domain through the spectral correlation density when cyclic frequency is zero. The result has higher signal to noise ratio (SNR). Compared with the traditional spectrum analytical methods, the impacts of background noise and interference are furthest reduced by cyclic statistics and the fault characteristic frequency of bearing is identified accurately.