Zhixiong Li

A fault diagnosis approach for gears using multidimensional features and intelligent classifier

Gear mechanisms are an important element in a variety of industrial applications and about 80% of the breakdowns of the transmission machinery are caused by the gear failure. Efficient incipient fault detection and accurate fault diagnosis are therefore critical to machinery normal operation. A new hybrid intelligent diagnosis method is proposed in this work to identify multiple categories of gear defection. In this method, wavelet packet transform (WPT), empirical mode decomposition (EMD) and Wigner-Ville distributions (WVD), combined with autoregressive (AR) model algorithm, were performed on gear vibration signals to extract useful fault characteristic information. Then, multidimensional feature sets including energy distribution, statistical features and AR parameters were obtained to represent gear operation conditions from different perspectives. The nonlinear dimensionality reduction algorithm, i.e. isometric mapping (Isomap), was employed in statistics to mine the intrinsic structure of the feature space in a low-dimensional space, and thus to speed up the training of the probabilistic neural network (PNN) classifier and enhance its diagnosis accuracy. Experiments with different gear faults were conducted, and the vibration signals were measured under different drive speeds and loads. The analysis results indicate that the proposed method is feasible and effective in the gear multi-fault diagnosis, and the isolation of different gear conditions, including normal, single crack, compound fault of wear and spalling, etc., has been accomplished. Since the recognition results are available directly from the output of PNN, the proposed diagnosis technique provides the possibility to fulfill the automatic recognition on gear multiple faults

Gear Multi-Faults Diagnosis of a Rotating Machinery Based on Independent Component Analysis and Fuzzy K-Nearest Neighbor

Gearboxes are extensively used in various areas including aircraft, mining, manufacturing, and agriculture, etc. The breakdowns of the gearbox are mostly caused by the gear failures. It is therefore crucial for engineers and researchers to monitor the gear conditions in time in order to prevent the malfunctions of the plants. In this paper, a condition monitoring and faults identification technique for rotating machineries based on independent component analysis (ICA) and fuzzy k-nearest neighbor (FKNN) is described. In the diagnosis process, the ICA was initially employed to separate characteristic vibration signal and interference vibration signal from the parallel time series obtained from multi-channel accelerometers mounted on different positions of the gearbox. The wavelet transform (WT) and autoregressive (AR) model method then were performed as the feature extraction technique to attain the original feature vector of the characteristic signal. Meanwhile, the ICA was used again to reduce the dimensionality of the original feature vector. Hence, the useless information in the feature vector could be removed. Finally, the FKNN algorithm was implemented in the pattern recognition process to identify the conditions of the gears of interest. The experimental results suggest that the sensitive fault features can be extracted efficiently after the ICA processing, and the proposed diagnostic system is effective for the gear multi-faults diagnosis, including the gear crack failure, pitting failure, gear tooth broken, compound fault of wear and spalling, etc. In addition, the proposed method can achieve higher performance than that without ICA processing with respect to the classification rate.

Flight of the Chinese white pine beetle (Coleoptera: Scolytidae) in relation to sex, body weight and energy reserve

The flight distance, flight time and individual flight activities of males and females of Dendroctonus armandi were recorded during 96-h flight trials using a flight mill system. The body weight, glucose, glycogen and lipid content of four treatments (naturally emerged, starved, phloem-fed and water-fed) were compared among pre-flight, post-flight and unflown controls. There was no significant difference between males and females in total flight distance and flight time in a given 24-h period. The flight distance and flight time of females showed a significant linear decline as the tethered flying continued, but the sustained flight ability of females was better than that of males. The females had higher glycogen and lipid content than the males; however, there was no significant difference between both sexes in glucose content. Water-feeding and phloem-feeding had significant effects on longevity, survival days and flight potential of D. armandi, which resulted in longer feeding days, poorer flight potential and lower energy substrate content. Our results demonstrate that flight distances in general do not differ between water-fed and starved individuals, whereas phloem-fed females and males fly better than water-fed and starved individuals.

A new method of nonlinear feature extraction for multi-fault diagnosis of rotor systems

Rotor systems have been extensively used in a variety of industrial applications. However an unexpected failure may cause a break down of the rotational machinery, resulting in production and significant economic losses. Efficient incipient fault diagnosis is therefore critical to the machinery normal operation. Noise and vibration analysis is popular and effective for the rotor fault diagnosis. One of the key procedures in the fault diagnosis is feature extraction and selection. Literature review indicates that only limited research considered the nonlinear property of the feature space by the use of manifold learning algorithms in the field of mechanical fault diagnosis, and nonlinear feature extraction for rotor multi-fault detection has not been studied. This paper reports a new development based on a novel supervised manifold learning algorithm (adaptive locally linear embedding) applied to nonlinear feature extraction for rotor multiple defects identification. The adaptive locally linear embedding (ALLE) combines with the adaptive nearest neighbour algorithm and supervised locally linear embedding (LLE) to provide an adaptive supervised learning. Hence, distinct nonlinear features could be extracted from high-dimensional dataset effectively. Based on ALLE, a new fault diagnosis approach has been proposed. The independent component analysis (ICA) was firstly employed to separate the faulty components of the rotor vibration from the observation data. Then wavelet transform (WT) was used to decompose the recovered signals, and statistical features of frequency bands were hence calculated. Lastly, ALLE was applied to learn the low-dimensional intrinsic structure of the original feature space. The experiments on vibration data of single and coupled rotor faults have demonstrated that sensitive fault features can be extracted efficiently after the ICA-WT-ALLE processing, and the proposed diagnostic system is effective for the multi-fault identification of the rotor system. Furthermore, the proposed method achieves higher performance in terms of the classification rate than other feature extraction methods such as principal component analysis (PCA) and locally linear embedding (LLE).

Numerical Simulation of Rock Breakage Modes under Confining Pressures in Deep Mining: An Experimental Investigation

The cutting efficiency in underground excavations relies on the optimum parameters of the cutting tool and the cutting process. However, the optimization of the cutting tool design and the cutting process is a challenge and requires knowledge about the tool-rock interaction. This paper aims to investigate the tool-rock interaction using a rock cutting mathematical model. The confining pressure was considered in the rock cutting model with conical cutters and the discrete element method was adopted to calculate the dynamics of the rock breakage of this model. Graded particle assemblies were created, calibrated, and compressed in the horizontal direction with a certain confining pressure. Afterwards, the initiation and propagation of cracks during the rock cutting processes were recorded. A series of small-scale rock cutting tests were also carried out to verify the numerical model. The analysis results demonstrate that: 1) the confining pressure induced larger cutting force than that in the unconfined condition; 2) with increase of the confining pressure, the rock failure mode experienced predominantly brittle to predominantly ductile failure; and 3) there was a critical confining pressure/compressive strength ratio of 0.53 when the transition of failure mode occurred.

Nanofluid turbulent flow in a pipe under the effect of twisted tape with alternate axis

In this research, nanofluid heat transfer enhancement in a pipe by means of twisted tape with alternate axis is presented. Finite volume method is selected as simulation tool. Influences of revolution angle and Reynolds number on nanofluid hydrothermal treatment have been demonstrated. Suitable formulas for Nusselt number and Darcy factor are provided. Results prove that temperature gradient augments with enhance of revolution angle because of increase in secondary flow but pressure loss augments with rise of revolution angle.

Gear faults diagnosis based on wavelet-AR model and PCA

Gear mechanisms are an important element in a variety of industrial applications and about 80% of the breakdowns of the transmission machinery are caused by the gear failure. Efficient incipient faults detection and accurate faults diagnosis are therefore critical to machinery normal operation. The use of mechanical vibration signals for fault diagnosis is significant and effective due to advances in the progress of digital signal processing techniques. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-faults diagnosis was presented in this paper based on the wavelet-Autoregressive (AR) model and Principal Component Analysis (PCA) method. The virtual prototype simulation and the experimental test were firstly carried out and the comparison results prove that the traditional Fast Fourier Transform Algorithm (FFT) analysis is not appropriate for the gear fault detection and identification. Then the wavelet-AR model was applied to extract the feature sets of the gear fault vibration data. In this procedure, the wavelet transform was used to decompose and de-noise the original signal to obtain fault signals, and the fault type information was extracted by the AR parameters. In order to eliminate the redundant fault features, the PCA was furthermore adopted to fuse the AR parameters into one characteristic to enhance the fault defection and identification. The experimental results indicate that the proposed method based on the wavelet-AR model and PCA is feasible and reliable in the gear multi-faults signal diagnosis, and the isolation of different gear conditions, including normal, single crack, single wear, compound fault of wear and spalling etc., has been effectively accomplished.

A Fault Diagnosis Method of Rolling Bearing through Wear Particle and Vibration Analyses

Wear particle and vibration analysis are the two main condition monitoring techniques for machinery maintenance and fault diagnosis in industry. Due to the complex nature of machinery, these two techniques can only diagnose about 30% to 40% of faults when used independently. Therefore, it is critical to integrate vibration analysis and wear particle analysis to provide a more effective maintenance program. This paper presents a new fault diagnosis approach of rolling bearings via the combination of vibration analysis and wear particle analysis. Both the tribological and vibrant information of the rolling bearings with typical faults were collected by an experimental test rig. Wear particle analysis was applied to the oil samples to obtain the wear particle number and size distribution, the particle texture and the chemical compositions, etc. Vibration analysis was used to get the time and frequency characteristics of the vibration data. Then, an intelligent data fusion method based on the genetic algorithm based fuzzy neural network was employed to identify the rolling bearing conditions. The analysis results suggest that the proposed method is more feasible and effective for the rolling bearing fault diagnosis than separated use of the two techniques with respect to the classification rate, and thus has application importance.

Difference equation based empirical mode decomposition with application to separation enhancement of multi-fault vibration signals

Abstract Empirical mode decomposition (EMD) has been applied to various applications in signal processing. However, EMD is susceptible to close mode characteristic frequencies and noise, resulting in the problem of mode mixing. The performance of multi-fault detection in gearboxes will be significantly degraded due to mode mixing in the vibration analysis. Hence, this paper presents a new method to address the mode mixing problem in EMD based gearbox multi-fault diagnosis. In this new method, the differential operation is introduced into the decomposition of the intrinsic mode functions. The decomposition ability of close frequency bands can be improved by the differential operation, and hence, the differential EMD can better identify the modes with close characteristic frequencies than its non-differential counterpart. In addition, time synchronous averaging (TSA) is combined with the differential EMD to address the noise issue. Thus, the proposed TAS and differential EMD based method (TDEMD) can solve the mode mixing problem to provide effective multi-fault detection for gearboxes. The TDEMD has been tested experimentally using vibration data collected from a gearbox with concurrent defects on two different gears. Results showed effective detection of gear multiple faults.

A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine

During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.