As a type of reciprocating machine, the reciprocating compressor has a compact structure and many excitation sources.Once the small end bearing of the connecting rod is worn, it is easy to cause the sintering of the b...As a type of reciprocating machine, the reciprocating compressor has a compact structure and many excitation sources.Once the small end bearing of the connecting rod is worn, it is easy to cause the sintering of the bearing and the abnormal vibration of the body.Based on the characteristics of poor lubrication state and complex force of connecting rod small head bearing, a mixed lubrication model considering oil groove feed was established, and the dynamic simulation of the reciprocating compressor model with lubricated bearings was carried out;considering different speeds and gas load conditions, the law of the impact of the eigenvalues changing with working conditions was explored.The fault simulation experiment was carried out by selecting representative working conditions, which verified the correctness of the simulation method.The study found that two contact collisions between the pin and the bearing bush occurred in one cycle, the collision impact was more severe under the wear fault, and the existence of the gap made the dynamic response more sensitive to the change of working conditions.This research provides ideas for the location and feature extraction of fault symptom signal angular segments in the process of complex measured signal processing.展开更多
Theoretical analysis and field monitoring show that lateral vibration has very important effect on casing wear in deep & ultra-deep well drilling. The wear mechanism of casing under impact-sliding work conditions ...Theoretical analysis and field monitoring show that lateral vibration has very important effect on casing wear in deep & ultra-deep well drilling. The wear mechanism of casing under impact-sliding work conditions has been investigated and many experiments have been completed with a newly developed full-scale casing wear test machine. Test results present that adhesion wear, contact fatigue, and grinding abrasion are the main wear mechanisms under impact-sliding test conditions. The friction coefficient and linear wear rate of the casing rise obviously with an increase in impact load. And the larger the impact load, the rougher the worn surface of the casing. The linear wear rate decreased slightly but the average friction coefficient increased slightly with an increase in impact frequency under an impact load of 2,500 N. Both the linear wear rate of the casing and the average friction coefficient increased substantially with an increase in impact frequency under an impact load of 4,000 N. Under lower impact load conditions, grinding abrasion and contact fatigue are the main mechanisms of casing wear; under higher impact load conditions, adhesion wear and contact fatigue are the main mechanisms of casing wear.展开更多
Wear particles are inevitably occurred from moving parts, such as a piston-cylinder made from steel or hybrid materials. And a durability of these parts must be evaluated. The wear particle analysis has been known as ...Wear particles are inevitably occurred from moving parts, such as a piston-cylinder made from steel or hybrid materials. And a durability of these parts must be evaluated. The wear particle analysis has been known as a very effective method to foreknow and decide a moving situation and a damage of machine parts by using the digital computer image processing. But it is not laid down to calculate shape parameters of wear particle and wear volume. In order to apply image processing method in a durability evaluation of machine parts, it needs to verify the reliability of the calculated data by the image processing and to lay down the number of images and the amount of wear particles in one image. In this work, the lubricated friction experiment was carried out in order to establish the optimum image capture with the 1045 specimen under experiment condition. The wear particle data were calculated differently according to the number of image and the amount of wear particle in one image. The results show that capturing conditions need to be more than 140 wear particles in one image and over 40 images for the reliable data. Thus, the capturing method of wear particles images was optimized for condition diagnosis of machine moving parts.展开更多
The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the s...The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.展开更多
This paper presents a novel approach to investigate the relations between drilling signals and bit wear condition in real world full-scale mining operations.This research addresses the increasing demand for automation...This paper presents a novel approach to investigate the relations between drilling signals and bit wear condition in real world full-scale mining operations.This research addresses the increasing demand for automation in mining to increase the efficiency,safety,and ability to work in harsh environments.A crucial issue in fully autonomous unmanned drilling is to have a system to detect the bit wear condition through the drilling signals analysis in real time.In this work,based on extensive field studies,a novel qualitative method for tricone bit wear state classification is developed and introduced.The relations between drilling vibration as well as electric motor current signals and bit wear are investigated and bit failure vibration frequencies,regardless of the geological conditions,are introduced.Bit failure frequencies are experimentally investigated and analytically calculated.Finally,the effect of bit design parameters on the failure frequencies is presented for the application of bit wear condition monitoring and bit failure prediction.展开更多
Wheel polygonal wear is a common and severe defect,which seriously threatens the running safety and reliability of a railway vehicle especially a locomotive.Due to non-stationary running conditions(e.g.,traction and b...Wheel polygonal wear is a common and severe defect,which seriously threatens the running safety and reliability of a railway vehicle especially a locomotive.Due to non-stationary running conditions(e.g.,traction and braking)of the locomotive,the passing frequencies of a polygonal wheel will exhibit time-varying behaviors,which makes it too difficult to effectively detect the wheel defect.Moreover,most existing methods only achieve qualitative fault diagnosis and they cannot accurately identify defect levels.To address these issues,this paper reports a novel quantitative method for fault detection of wheel polygonization under non-stationary conditions based on a recently proposed adaptive chirp mode decomposition(ACMD)approach.Firstly,a coarse-to-fine method based on the time–frequency ridge detection and ACMD is developed to accurately estimate a time-varying gear meshing frequency and thus obtain a wheel rotating frequency from a vibration acceleration signal of a motor.After the rotating frequency is obtained,signal resampling and order analysis techniques are applied to an acceleration signal of an axle box to identify harmonic orders related to polygonal wear.Finally,the ACMD is combined with an inertial algorithm to estimate polygonal wear amplitudes.Not only a dynamics simulation but a field test was carried out to show that the proposed method can effectively detect both harmonic orders and their amplitudes of the wheel polygonization under non-stationary conditions.展开更多
This article introduces a novel approach for tricone bit wear condition monitoring and failure prediction for surface mining applications.A successful bit health monitoring system is essential to achieve fully autonom...This article introduces a novel approach for tricone bit wear condition monitoring and failure prediction for surface mining applications.A successful bit health monitoring system is essential to achieve fully autonomous blasthole drilling.In this research in-situ vibration signals were analyzed in timefrequency domain and signals trend during tricone bit life span were investigated and introduced to support the development of artificial intelligence(AI)models.In addition to the signal statistical features,wavelet packet energy distribution proved to be a powerful indicator for bit wear assessment.Backpropagation artificial neural network(ANN)models were designed,trained and evaluated for bit state classification.Finally,an ANN architecture and feature vector were introduced to classify the bit condition and predict the bit failure.展开更多
Effects of quenching temperature and cooling conditions(water cooling and 10%NaCl cooling)on microstructure and mechanical properties of a 0.2%Ti low alloy martensitic wear-resistant steel used for die casting ejector...Effects of quenching temperature and cooling conditions(water cooling and 10%NaCl cooling)on microstructure and mechanical properties of a 0.2%Ti low alloy martensitic wear-resistant steel used for die casting ejector plate were investigated.The results show that lath martensite can be obtained after austenitizing in the range of 860-980℃and then water cooling.With an increase in austenitizing temperature,the precipitate content gradually decreases.The precipitates are mainly composed of TiC and Ti4C2S2,and their total content is between 1.15wt.%and 1.64wt.%.The precipitate phase concentration by water-cooling is higher than that by10%NaCl cooling due to the lower cooling rate of water cooling.As the austeniting temperature increases,the hardness and tensile strength of both water cooled and 10%NaCl cooled steels firstly increase and then decrease.The experimental steel exhibits the best comprehensive mechanical properties after being austenitized at 900℃,cooled by 10%NaCl,and then tempered at 200℃.Its hardness,ultimate tensile strength,and wear rate reach551.4 HBW,1,438.2 MPa,and 0.48×10^(-2)mg·m^(-1),respectively.展开更多
The failure rate of crankpin bearing bush of diesel engine under complex working conditions such as high temperature,dynamic load and variable speed is high.After serious wear,it is easy to deteriorate the stress stat...The failure rate of crankpin bearing bush of diesel engine under complex working conditions such as high temperature,dynamic load and variable speed is high.After serious wear,it is easy to deteriorate the stress state of connecting rod body and connecting rod bolt,resulting in serious accidents such as connecting rod fracture and body damage.Based on the mixed lubrication characteristics of connecting rod big endbearing shell of diesel engine under high explosion pressure impact load,an improved mixed lubrication mechanism model is established,which considers the influence of viscoelastic micro deformation of bearing bush material,integrates the full film lubrication model and dry friction model,couples dynamic equation of connecting rod.Then the actual lubrication state of big end bearing shell is simulated numerically.Further,the correctness of the theoretical research results is verified by fault simulation experiments.The results show that the high-frequency impact signal with fixed angle domain characteristics will be generated after the serious wear of bearing bush and the deterioration of lubrication state.The fault feature capture and alarm can be realized through the condition monitoring system,which can be applied to the fault monitoring of connecting rod bearing bush of diesel engine in the future.展开更多
基金Supported by the National Natural Science Foundation of China (No.52101343)。
文摘As a type of reciprocating machine, the reciprocating compressor has a compact structure and many excitation sources.Once the small end bearing of the connecting rod is worn, it is easy to cause the sintering of the bearing and the abnormal vibration of the body.Based on the characteristics of poor lubrication state and complex force of connecting rod small head bearing, a mixed lubrication model considering oil groove feed was established, and the dynamic simulation of the reciprocating compressor model with lubricated bearings was carried out;considering different speeds and gas load conditions, the law of the impact of the eigenvalues changing with working conditions was explored.The fault simulation experiment was carried out by selecting representative working conditions, which verified the correctness of the simulation method.The study found that two contact collisions between the pin and the bearing bush occurred in one cycle, the collision impact was more severe under the wear fault, and the existence of the gap made the dynamic response more sensitive to the change of working conditions.This research provides ideas for the location and feature extraction of fault symptom signal angular segments in the process of complex measured signal processing.
基金supported by the National Natural Science Foundation of China (No.50475037)
文摘Theoretical analysis and field monitoring show that lateral vibration has very important effect on casing wear in deep & ultra-deep well drilling. The wear mechanism of casing under impact-sliding work conditions has been investigated and many experiments have been completed with a newly developed full-scale casing wear test machine. Test results present that adhesion wear, contact fatigue, and grinding abrasion are the main wear mechanisms under impact-sliding test conditions. The friction coefficient and linear wear rate of the casing rise obviously with an increase in impact load. And the larger the impact load, the rougher the worn surface of the casing. The linear wear rate decreased slightly but the average friction coefficient increased slightly with an increase in impact frequency under an impact load of 2,500 N. Both the linear wear rate of the casing and the average friction coefficient increased substantially with an increase in impact frequency under an impact load of 4,000 N. Under lower impact load conditions, grinding abrasion and contact fatigue are the main mechanisms of casing wear; under higher impact load conditions, adhesion wear and contact fatigue are the main mechanisms of casing wear.
基金Project supported by Research Funds from Dong-A University,Korea
文摘Wear particles are inevitably occurred from moving parts, such as a piston-cylinder made from steel or hybrid materials. And a durability of these parts must be evaluated. The wear particle analysis has been known as a very effective method to foreknow and decide a moving situation and a damage of machine parts by using the digital computer image processing. But it is not laid down to calculate shape parameters of wear particle and wear volume. In order to apply image processing method in a durability evaluation of machine parts, it needs to verify the reliability of the calculated data by the image processing and to lay down the number of images and the amount of wear particles in one image. In this work, the lubricated friction experiment was carried out in order to establish the optimum image capture with the 1045 specimen under experiment condition. The wear particle data were calculated differently according to the number of image and the amount of wear particle in one image. The results show that capturing conditions need to be more than 140 wear particles in one image and over 40 images for the reliable data. Thus, the capturing method of wear particles images was optimized for condition diagnosis of machine moving parts.
基金the National Key Research and Development Program of China(No.2020YFB1713500)the Natural Science Basic Research Program of Shaanxi(Grant No.2023JCYB289)+1 种基金the National Natural Science Foundation of China(Grant No.52175112)the Fundamental Research Funds for the Central Universities(Grant No.ZYTS23102).
文摘The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.
基金supports from Canada Natural Sciences and Engineering Research Council(NSERC-CRD grant#461514,NSERC-I2I grant#516232)McGill University Engine Centre+2 种基金Teck ResourcesArcelorMittalRotacan companies。
文摘This paper presents a novel approach to investigate the relations between drilling signals and bit wear condition in real world full-scale mining operations.This research addresses the increasing demand for automation in mining to increase the efficiency,safety,and ability to work in harsh environments.A crucial issue in fully autonomous unmanned drilling is to have a system to detect the bit wear condition through the drilling signals analysis in real time.In this work,based on extensive field studies,a novel qualitative method for tricone bit wear state classification is developed and introduced.The relations between drilling vibration as well as electric motor current signals and bit wear are investigated and bit failure vibration frequencies,regardless of the geological conditions,are introduced.Bit failure frequencies are experimentally investigated and analytically calculated.Finally,the effect of bit design parameters on the failure frequencies is presented for the application of bit wear condition monitoring and bit failure prediction.
基金This work is supported by the National Natural Science Foundation of China(Grant Nos.52005416,51735012,and 51825504)the Sichuan Science and Technology Program(Grant No.2020YJ0213)+1 种基金the Fundamental Research Funds for the Central Universities,SWJTU(Grant No.2682021CX091)the State Key Laboratory of Traction Power(Grant No.2020TPL-T 11).
文摘Wheel polygonal wear is a common and severe defect,which seriously threatens the running safety and reliability of a railway vehicle especially a locomotive.Due to non-stationary running conditions(e.g.,traction and braking)of the locomotive,the passing frequencies of a polygonal wheel will exhibit time-varying behaviors,which makes it too difficult to effectively detect the wheel defect.Moreover,most existing methods only achieve qualitative fault diagnosis and they cannot accurately identify defect levels.To address these issues,this paper reports a novel quantitative method for fault detection of wheel polygonization under non-stationary conditions based on a recently proposed adaptive chirp mode decomposition(ACMD)approach.Firstly,a coarse-to-fine method based on the time–frequency ridge detection and ACMD is developed to accurately estimate a time-varying gear meshing frequency and thus obtain a wheel rotating frequency from a vibration acceleration signal of a motor.After the rotating frequency is obtained,signal resampling and order analysis techniques are applied to an acceleration signal of an axle box to identify harmonic orders related to polygonal wear.Finally,the ACMD is combined with an inertial algorithm to estimate polygonal wear amplitudes.Not only a dynamics simulation but a field test was carried out to show that the proposed method can effectively detect both harmonic orders and their amplitudes of the wheel polygonization under non-stationary conditions.
基金The authors appreciate generous supports from Canada Natural Sciences and Engineering Research Council,McGill University Engine Centre as well as Faculty of Engineering.
文摘This article introduces a novel approach for tricone bit wear condition monitoring and failure prediction for surface mining applications.A successful bit health monitoring system is essential to achieve fully autonomous blasthole drilling.In this research in-situ vibration signals were analyzed in timefrequency domain and signals trend during tricone bit life span were investigated and introduced to support the development of artificial intelligence(AI)models.In addition to the signal statistical features,wavelet packet energy distribution proved to be a powerful indicator for bit wear assessment.Backpropagation artificial neural network(ANN)models were designed,trained and evaluated for bit state classification.Finally,an ANN architecture and feature vector were introduced to classify the bit condition and predict the bit failure.
文摘Effects of quenching temperature and cooling conditions(water cooling and 10%NaCl cooling)on microstructure and mechanical properties of a 0.2%Ti low alloy martensitic wear-resistant steel used for die casting ejector plate were investigated.The results show that lath martensite can be obtained after austenitizing in the range of 860-980℃and then water cooling.With an increase in austenitizing temperature,the precipitate content gradually decreases.The precipitates are mainly composed of TiC and Ti4C2S2,and their total content is between 1.15wt.%and 1.64wt.%.The precipitate phase concentration by water-cooling is higher than that by10%NaCl cooling due to the lower cooling rate of water cooling.As the austeniting temperature increases,the hardness and tensile strength of both water cooled and 10%NaCl cooled steels firstly increase and then decrease.The experimental steel exhibits the best comprehensive mechanical properties after being austenitized at 900℃,cooled by 10%NaCl,and then tempered at 200℃.Its hardness,ultimate tensile strength,and wear rate reach551.4 HBW,1,438.2 MPa,and 0.48×10^(-2)mg·m^(-1),respectively.
基金Supported by the National Natural Science Foundation of China(No.52101343)the Aeronautical Science Foundation(No.201834S9002).
文摘The failure rate of crankpin bearing bush of diesel engine under complex working conditions such as high temperature,dynamic load and variable speed is high.After serious wear,it is easy to deteriorate the stress state of connecting rod body and connecting rod bolt,resulting in serious accidents such as connecting rod fracture and body damage.Based on the mixed lubrication characteristics of connecting rod big endbearing shell of diesel engine under high explosion pressure impact load,an improved mixed lubrication mechanism model is established,which considers the influence of viscoelastic micro deformation of bearing bush material,integrates the full film lubrication model and dry friction model,couples dynamic equation of connecting rod.Then the actual lubrication state of big end bearing shell is simulated numerically.Further,the correctness of the theoretical research results is verified by fault simulation experiments.The results show that the high-frequency impact signal with fixed angle domain characteristics will be generated after the serious wear of bearing bush and the deterioration of lubrication state.The fault feature capture and alarm can be realized through the condition monitoring system,which can be applied to the fault monitoring of connecting rod bearing bush of diesel engine in the future.
