Impulse response provides important information about flaws in mechanical system. Deconvolution i... more Impulse response provides important information about flaws in mechanical system. Deconvolution is one system identification technique for fault detection when signals captured from bearings with and without flaw are both available. However effects of measurement systems and noise are obstacles to the technique. In the present study, a model, namely autoregressive-moving average (ARMA), is used to estimate vibration pattern of rolling element bearings for fault detection. The frequently used ARMA estimator cannot characterize non-Gaussian noise completely. Aimed at circumventing the inefficiency of the second-order statistics-based ARMA estimator, higher-order statistics (HOS) was introduced to ARMA estimator, which eliminates the effect of noise greatly and, therefore, offers more accurate estimation of the system. Furthermore, bispectrums of the estimated HOS-based ARMA models were subsequently applied to get clearer information. Impulse responses of signals captured from the test bearings without and with flaws and their bispectra were compared for the purpose of fault detection. The results demonstrated the excellent capability of this method in vibration signal processing and fault detection.
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2004
This paper proposes a novel technique for the condition monitoring of gearboxes based on a self-o... more This paper proposes a novel technique for the condition monitoring of gearboxes based on a self-organizing feature maps (SOF M ) network. In order to visualize the learned SOF M results more clearly, an improved method based on the uni ed distance matrix (U -matrix) method is presented, in which the overall topological information condensed into the map units is considered so as to project the high-dimensional input vectors into a two-dimensional space and give a better picture of their intrinsic structure than the original U -matrix method. The feature data extracted from industrial gearbox vibration signals measured under different operating conditions are analysed using the proposed technique. The results show that trained with the SOF M network and visualized with the improved method, the feature data are mapped into a two-dimensional space and formed clustering regions, each indicative of a speci c gearbox condition. Therefore, the gearbox operating condition with a fatigue crack or broken tooth compared with the normal condition is identi ed clearly. F urthermore, with the trajectory of the image points for the feature data in two-dimensional space, the variation of gearbox conditions is observed visually, and the development of gearbox early-stage failures is monitored in time.
ABSTRACT As one of the most commonly used manufacturing processes, stamping operations are applie... more ABSTRACT As one of the most commonly used manufacturing processes, stamping operations are applied to an extended range from centimeter-class parts to meter-class parts. The demand for the quality and productivity of stamping products is ever increasing. Hence, the extraction of the appropriate feature to implement on-line monitoring has been attempted. The vibration signals provide rich information for finding the dynamic behavior at high frequency vibration of the press. In this paper, an accelerometer has been employed in place of tonnage sensors or strain gauges to monitor the stamping process. In order to characterize the transient signal of the process, this work extracts the energy densities and frequency band energy (FBE) from the vibration signal. Owing to its inherent properties, the wavelet packet transformation decomposes the original signal into basis functions, with different energy distributions dominating in different time-frequency bands. Based on the experimental results, this suggests that extracting FBE from a vibration signal using a wavelet packet as a feature for fault diagnosis can, in practice be an effective approach for stamping process monitoring.
ABSTRACT This study presents an experimental study, including interfacial tension (IFT) measureme... more ABSTRACT This study presents an experimental study, including interfacial tension (IFT) measurements, sandpack flood tests, and microscopic studies, for investigating the effect of the addition of low molecular weight alcohols on heavy oil recovery during alkaline flooding. According to the IFT results, the addition of low molecular weight alcohols can be detrimental to IFT reduction for the alkaline/heavy oil system, due to the partitioning of the alcohol molecules at the oil–water interface reducing the interfacial space available for surfactant molecules. However, sandpack floods conducted with the addition of the low molecular weight alcohols show a marked improvement in oil recovery over the alkaline-only flooding. The incremental oil recovery increases with the alcohol chain length from methanol to n-pentanol, but for the less water-soluble isoamyl alcohol and n-hexanol, the incremental oil recovery starts to decrease. The microscopic studies indicate that the alcohol additives can accelerate the reaction rate to produce large amounts of small water droplets inside the oil phase (W/O droplet flow), which reinforces the Jamin effect to improve sweep efficiency. Meanwhile, the addition of low molecular weight alcohols can also lead to the reduction in water-in-oil (W/O) emulsion viscosity, which contributes to the mobilization of trapped W/O emulsions, thereby improving the displacement efficiency of the alkaline flooding process.
Engineering Applications of Artificial Intelligence, 2002
ABSTRACT Owing to the ever-increased demand for product quality improvement and production cost r... more ABSTRACT Owing to the ever-increased demand for product quality improvement and production cost reduction, on-line monitoring of stamping operations has become a common practice in shop floors around the world today. Based on a market survey, for most stamping processes, monitoring systems use tonnage signals and/or strain signals. Recently, a few attempts have been reported using acceleration signals as they contain rich information and are relatively inexpensive. However, it is known that acceleration signals are vulnerable to the noise disturbances, and hence are less robust. This paper presents a study that uses bispectrum to analyze the acceleration signals. It is shown that the bispectrum can suppress Gaussian color noise to boost the signal-to-noise ratio. It also extracts the features of the signal that are related to the defective parts (such as material too thick or slug). The experimental results demonstrate that the method presented is effective and has a good potential for applications in shop floor. We also present new method for reducing the computation load in the process.
ABSTRACT Gear fault feature extraction using integrated bispectrum is investigated in this paper.... more ABSTRACT Gear fault feature extraction using integrated bispectrum is investigated in this paper. Integrated bispectrum is a kind of 1-D higher order spectra, it is obtained by integrating out one of the two frequency variables of the bispectrum. Integrated bispectrum provides more of signal features than bispectrum slices do. Moreover, it is more convenient to use than all kinds of 1-D bispectrum slices. Experimental results show that integrated bispectrum is more effective than bispectrum slices in extracting mechanical fault features.
This paper presents a new method for fault diagnosis using a newly developed method, support vect... more This paper presents a new method for fault diagnosis using a newly developed method, support vector machine (SVM). First, the basic theory of the SVM is briefly reviewed. Next, a fast implementation algorithm is given. Then the method is applied for the fault diagnosis in sheet metal stamping processes. According to the tests on two different examples, one is a simple blanking and the other is a progressive operation, the new method is very effective. In both cases, its success rate is over 96.5%. In comparison, the success rate of the popular artificial neural network (ANN) is just 93.3%. In addition, the new method requires only few training samples, which is an attractive feature for shop floor applications. r From a theoretical point of view, fault diagnosis problems have a close relationship with the pattern recognition, in which one wishes to classify the data to pre-defined classes. Hence, pattern classification methods, including machine learning (ML) and statistics, can be applied.
The minimally invasive approach of arthroscopy means faster recover and less pain for the patient... more The minimally invasive approach of arthroscopy means faster recover and less pain for the patient than that of open surgery, but the skills required by the surgeon are radically different. Although a number of arthroscopic training techniques are available, all have problems with cost, availability, etc. Virtual reality based training system has been proofed a promising technology in many fields including pilot training. In recent years, virtual reality technique found its application in endoscopic surgeries. In this paper, a virtual reality based training system is presented. Mechanical design, kinematics, dexterity measure and control loop of the haptic device are investigated. Organ mesh generation, tissue deformation simulation and collision detection are also discussed. Finally a virtual reality based arthroscopic surgery simulator is developed. Testing and evaluation results for the prototype training system, was very positive and satisfactory.
As the Machinery Condition Monitoring and Fault Diagnosis Systems (MCMFDSs) are more and more com... more As the Machinery Condition Monitoring and Fault Diagnosis Systems (MCMFDSs) are more and more complex, the design and development of these systems are becoming a challenge. The best way to manage the complexity and risk is to abstract and model them. This paper presents a new method of modeling Web-based Remote Monitoring and Fault Diagnosis Systems (WRMFDSs) with Unified Modeling Language (UML). A component framework model is put forward. A highly maintainable WRMFDS with three reusable component packages was developed using component-based programming. This paper, which studies a reusable WRMFDS model, aims at making such advanced information technologies be used widely in the condition monitoring and fault diagnosis domain, it can give developers a paradigm to accomplish the similar systems.
In this paper, a novel global non-destructive evaluation (NDE) technique based on information fus... more In this paper, a novel global non-destructive evaluation (NDE) technique based on information fusion is proposed to diagnose loosening fault of clamping support. Two feature extraction methods are used to extract feature, which are wavelet package transform and power spectrum density analysis. During diagnosing loosening fault, two local decisions are made by using WP feature and PSD feature respectively. Then the two features are fused to make another local decision. Lastly, the three local decisions are fused to make global decision. The information fusion result have high correct diagnosis ratio and good antinoise performance. The correct diagnosis ratios with no noise and random noise reach 94.3% and 88.6% respectively.
A new model for vibration analysis of a crankshaft with a slant crack in crankpin is proposed, an... more A new model for vibration analysis of a crankshaft with a slant crack in crankpin is proposed, and the influence of crack depth on the transient response of a cracked crankshaft is investigated. A slant cracked shaft element is developed by deducing the local flexibility due to a slant crack. The frequently occurred slant crack in crankpin is studied, and a new finite element model of crankshaft including the slant crack in crankpin, which combines the slant cracked shaft element and Timoshenko beam elements, is derived. The support of engine block and the switching behaviour of the crack are considered, and the non-linear equation of motion for cracked crankshaft-bearing system is set up in a rotating coordinate system. The motion of a crankshaft of a four in-line cylinder engine with and without an initial crack is simulated. The influence of the crack depth on the transient response is investigated. The numerical simulation demonstrates that the current model is valid for simulating the motion of cracked crankshaft system. The results show that a useful foundation is laid for crack detection of crankshaft. r
A new method for simulating nonlinear motion of cracked crankshaft is proposed, and the transient... more A new method for simulating nonlinear motion of cracked crankshaft is proposed, and the transient vibration response of a cracked crankshaft is evaluated and analyzed. First, the crankshaft without crack is simplified as a finite element model based on spatial Timoshenko beam element, and the vibration modes of the crankshaft are calculated and compared with the results presented in other published literatures. Then, the frequently occurred crack in crankpin-web fillet region is studied. According to the characteristic of this kind of crack, a new spatial crack beam element is developed, and a cracked crankshaft model, which combines crack beam element and Timoshenko beam elements, is established. Subsequently, the breathing behavior of the crack under operating condition is discussed, and the nonlinear equation of motion of cracked crankshaft is set up. Finally, the transient vibration response of the cracked crankshaft under fire condition is evaluated, and the influence of the crack depth on the vibration response of torsion, translation and bending are analyzed. The modeling and analysis procedures are applied to a crankshaft system of a four in-line cylinder engine. This investigation provides a useful tool for the vibration analysis and crack detection of cracked crankshaft system. r
This paper proposes a novel cyclic statistics based artificial neural network for early fault dia... more This paper proposes a novel cyclic statistics based artificial neural network for early fault diagnosis of rolling element bearing, via which the real time domain signals obtained from a test rig are preprocessed by cyclic statistics to perform monitoring fault diagnosis. Three kinds of familiar faults are intentionally introduced in order to investigate typical rolling element bearing faults. The testing results are presented and discussed with examples of real time data collected from the test rig.
Impulse response provides important information about flaws in mechanical system. Deconvolution i... more Impulse response provides important information about flaws in mechanical system. Deconvolution is one system identification technique for fault detection when signals captured from bearings with and without flaw are both available. However effects of measurement systems and noise are obstacles to the technique. In the present study, a model, namely autoregressive-moving average (ARMA), is used to estimate vibration pattern of rolling element bearings for fault detection. The frequently used ARMA estimator cannot characterize non-Gaussian noise completely. Aimed at circumventing the inefficiency of the second-order statistics-based ARMA estimator, higher-order statistics (HOS) was introduced to ARMA estimator, which eliminates the effect of noise greatly and, therefore, offers more accurate estimation of the system. Furthermore, bispectrums of the estimated HOS-based ARMA models were subsequently applied to get clearer information. Impulse responses of signals captured from the test bearings without and with flaws and their bispectra were compared for the purpose of fault detection. The results demonstrated the excellent capability of this method in vibration signal processing and fault detection.
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2004
This paper proposes a novel technique for the condition monitoring of gearboxes based on a self-o... more This paper proposes a novel technique for the condition monitoring of gearboxes based on a self-organizing feature maps (SOF M ) network. In order to visualize the learned SOF M results more clearly, an improved method based on the uni ed distance matrix (U -matrix) method is presented, in which the overall topological information condensed into the map units is considered so as to project the high-dimensional input vectors into a two-dimensional space and give a better picture of their intrinsic structure than the original U -matrix method. The feature data extracted from industrial gearbox vibration signals measured under different operating conditions are analysed using the proposed technique. The results show that trained with the SOF M network and visualized with the improved method, the feature data are mapped into a two-dimensional space and formed clustering regions, each indicative of a speci c gearbox condition. Therefore, the gearbox operating condition with a fatigue crack or broken tooth compared with the normal condition is identi ed clearly. F urthermore, with the trajectory of the image points for the feature data in two-dimensional space, the variation of gearbox conditions is observed visually, and the development of gearbox early-stage failures is monitored in time.
ABSTRACT As one of the most commonly used manufacturing processes, stamping operations are applie... more ABSTRACT As one of the most commonly used manufacturing processes, stamping operations are applied to an extended range from centimeter-class parts to meter-class parts. The demand for the quality and productivity of stamping products is ever increasing. Hence, the extraction of the appropriate feature to implement on-line monitoring has been attempted. The vibration signals provide rich information for finding the dynamic behavior at high frequency vibration of the press. In this paper, an accelerometer has been employed in place of tonnage sensors or strain gauges to monitor the stamping process. In order to characterize the transient signal of the process, this work extracts the energy densities and frequency band energy (FBE) from the vibration signal. Owing to its inherent properties, the wavelet packet transformation decomposes the original signal into basis functions, with different energy distributions dominating in different time-frequency bands. Based on the experimental results, this suggests that extracting FBE from a vibration signal using a wavelet packet as a feature for fault diagnosis can, in practice be an effective approach for stamping process monitoring.
ABSTRACT This study presents an experimental study, including interfacial tension (IFT) measureme... more ABSTRACT This study presents an experimental study, including interfacial tension (IFT) measurements, sandpack flood tests, and microscopic studies, for investigating the effect of the addition of low molecular weight alcohols on heavy oil recovery during alkaline flooding. According to the IFT results, the addition of low molecular weight alcohols can be detrimental to IFT reduction for the alkaline/heavy oil system, due to the partitioning of the alcohol molecules at the oil–water interface reducing the interfacial space available for surfactant molecules. However, sandpack floods conducted with the addition of the low molecular weight alcohols show a marked improvement in oil recovery over the alkaline-only flooding. The incremental oil recovery increases with the alcohol chain length from methanol to n-pentanol, but for the less water-soluble isoamyl alcohol and n-hexanol, the incremental oil recovery starts to decrease. The microscopic studies indicate that the alcohol additives can accelerate the reaction rate to produce large amounts of small water droplets inside the oil phase (W/O droplet flow), which reinforces the Jamin effect to improve sweep efficiency. Meanwhile, the addition of low molecular weight alcohols can also lead to the reduction in water-in-oil (W/O) emulsion viscosity, which contributes to the mobilization of trapped W/O emulsions, thereby improving the displacement efficiency of the alkaline flooding process.
Engineering Applications of Artificial Intelligence, 2002
ABSTRACT Owing to the ever-increased demand for product quality improvement and production cost r... more ABSTRACT Owing to the ever-increased demand for product quality improvement and production cost reduction, on-line monitoring of stamping operations has become a common practice in shop floors around the world today. Based on a market survey, for most stamping processes, monitoring systems use tonnage signals and/or strain signals. Recently, a few attempts have been reported using acceleration signals as they contain rich information and are relatively inexpensive. However, it is known that acceleration signals are vulnerable to the noise disturbances, and hence are less robust. This paper presents a study that uses bispectrum to analyze the acceleration signals. It is shown that the bispectrum can suppress Gaussian color noise to boost the signal-to-noise ratio. It also extracts the features of the signal that are related to the defective parts (such as material too thick or slug). The experimental results demonstrate that the method presented is effective and has a good potential for applications in shop floor. We also present new method for reducing the computation load in the process.
ABSTRACT Gear fault feature extraction using integrated bispectrum is investigated in this paper.... more ABSTRACT Gear fault feature extraction using integrated bispectrum is investigated in this paper. Integrated bispectrum is a kind of 1-D higher order spectra, it is obtained by integrating out one of the two frequency variables of the bispectrum. Integrated bispectrum provides more of signal features than bispectrum slices do. Moreover, it is more convenient to use than all kinds of 1-D bispectrum slices. Experimental results show that integrated bispectrum is more effective than bispectrum slices in extracting mechanical fault features.
This paper presents a new method for fault diagnosis using a newly developed method, support vect... more This paper presents a new method for fault diagnosis using a newly developed method, support vector machine (SVM). First, the basic theory of the SVM is briefly reviewed. Next, a fast implementation algorithm is given. Then the method is applied for the fault diagnosis in sheet metal stamping processes. According to the tests on two different examples, one is a simple blanking and the other is a progressive operation, the new method is very effective. In both cases, its success rate is over 96.5%. In comparison, the success rate of the popular artificial neural network (ANN) is just 93.3%. In addition, the new method requires only few training samples, which is an attractive feature for shop floor applications. r From a theoretical point of view, fault diagnosis problems have a close relationship with the pattern recognition, in which one wishes to classify the data to pre-defined classes. Hence, pattern classification methods, including machine learning (ML) and statistics, can be applied.
The minimally invasive approach of arthroscopy means faster recover and less pain for the patient... more The minimally invasive approach of arthroscopy means faster recover and less pain for the patient than that of open surgery, but the skills required by the surgeon are radically different. Although a number of arthroscopic training techniques are available, all have problems with cost, availability, etc. Virtual reality based training system has been proofed a promising technology in many fields including pilot training. In recent years, virtual reality technique found its application in endoscopic surgeries. In this paper, a virtual reality based training system is presented. Mechanical design, kinematics, dexterity measure and control loop of the haptic device are investigated. Organ mesh generation, tissue deformation simulation and collision detection are also discussed. Finally a virtual reality based arthroscopic surgery simulator is developed. Testing and evaluation results for the prototype training system, was very positive and satisfactory.
As the Machinery Condition Monitoring and Fault Diagnosis Systems (MCMFDSs) are more and more com... more As the Machinery Condition Monitoring and Fault Diagnosis Systems (MCMFDSs) are more and more complex, the design and development of these systems are becoming a challenge. The best way to manage the complexity and risk is to abstract and model them. This paper presents a new method of modeling Web-based Remote Monitoring and Fault Diagnosis Systems (WRMFDSs) with Unified Modeling Language (UML). A component framework model is put forward. A highly maintainable WRMFDS with three reusable component packages was developed using component-based programming. This paper, which studies a reusable WRMFDS model, aims at making such advanced information technologies be used widely in the condition monitoring and fault diagnosis domain, it can give developers a paradigm to accomplish the similar systems.
In this paper, a novel global non-destructive evaluation (NDE) technique based on information fus... more In this paper, a novel global non-destructive evaluation (NDE) technique based on information fusion is proposed to diagnose loosening fault of clamping support. Two feature extraction methods are used to extract feature, which are wavelet package transform and power spectrum density analysis. During diagnosing loosening fault, two local decisions are made by using WP feature and PSD feature respectively. Then the two features are fused to make another local decision. Lastly, the three local decisions are fused to make global decision. The information fusion result have high correct diagnosis ratio and good antinoise performance. The correct diagnosis ratios with no noise and random noise reach 94.3% and 88.6% respectively.
A new model for vibration analysis of a crankshaft with a slant crack in crankpin is proposed, an... more A new model for vibration analysis of a crankshaft with a slant crack in crankpin is proposed, and the influence of crack depth on the transient response of a cracked crankshaft is investigated. A slant cracked shaft element is developed by deducing the local flexibility due to a slant crack. The frequently occurred slant crack in crankpin is studied, and a new finite element model of crankshaft including the slant crack in crankpin, which combines the slant cracked shaft element and Timoshenko beam elements, is derived. The support of engine block and the switching behaviour of the crack are considered, and the non-linear equation of motion for cracked crankshaft-bearing system is set up in a rotating coordinate system. The motion of a crankshaft of a four in-line cylinder engine with and without an initial crack is simulated. The influence of the crack depth on the transient response is investigated. The numerical simulation demonstrates that the current model is valid for simulating the motion of cracked crankshaft system. The results show that a useful foundation is laid for crack detection of crankshaft. r
A new method for simulating nonlinear motion of cracked crankshaft is proposed, and the transient... more A new method for simulating nonlinear motion of cracked crankshaft is proposed, and the transient vibration response of a cracked crankshaft is evaluated and analyzed. First, the crankshaft without crack is simplified as a finite element model based on spatial Timoshenko beam element, and the vibration modes of the crankshaft are calculated and compared with the results presented in other published literatures. Then, the frequently occurred crack in crankpin-web fillet region is studied. According to the characteristic of this kind of crack, a new spatial crack beam element is developed, and a cracked crankshaft model, which combines crack beam element and Timoshenko beam elements, is established. Subsequently, the breathing behavior of the crack under operating condition is discussed, and the nonlinear equation of motion of cracked crankshaft is set up. Finally, the transient vibration response of the cracked crankshaft under fire condition is evaluated, and the influence of the crack depth on the vibration response of torsion, translation and bending are analyzed. The modeling and analysis procedures are applied to a crankshaft system of a four in-line cylinder engine. This investigation provides a useful tool for the vibration analysis and crack detection of cracked crankshaft system. r
This paper proposes a novel cyclic statistics based artificial neural network for early fault dia... more This paper proposes a novel cyclic statistics based artificial neural network for early fault diagnosis of rolling element bearing, via which the real time domain signals obtained from a test rig are preprocessed by cyclic statistics to perform monitoring fault diagnosis. Three kinds of familiar faults are intentionally introduced in order to investigate typical rolling element bearing faults. The testing results are presented and discussed with examples of real time data collected from the test rig.
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