Dynamic Vision-Based Machinery Fault Diagnosis With Cross-Modality Feature Alignment

Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades,and the vibration acceleration data collected by contact accelerometers have been widely investigated.In many industrial scenarios,contactless sensors are more preferred.The event camera is an emerging bio-inspired technology for vision sensing,which asynchronously records per-pixel brightness change polarity with high temporal resolution and low latency.It offers a promising tool for contactless machine vibration sensing and fault diagnosis.However,the dynamic vision-based methods suffer from variations of practical factors such as camera position,machine operating condition,etc.Furthermore,as a new sensing technology,the labeled dynamic vision data are limited,which generally cannot cover a wide range of machine fault modes.Aiming at these challenges,a novel dynamic vision-based machinery fault diagnosis method is proposed in this paper.It is motivated to explore the abundant vibration acceleration data for enhancing the dynamic vision-based model performance.A crossmodality feature alignment method is thus proposed with deep adversarial neural networks to achieve fault diagnosis knowledge transfer.An event erasing method is further proposed for improving model robustness against variations.The proposed method can effectively identify unseen fault mode with dynamic vision data.Experiments on two rotating machine monitoring datasets are carried out for validations,and the results suggest the proposed method is promising for generalized contactless machinery fault diagnosis.

Condition Monitoring and Fault Diagnosis Based on Rough Set Theory

In order to raise the efficiency,automatization and intelligentization of condition monitoring and fault diagnosis for complex equipment systems,rough set theory is used to the field. A feature reduction algorithm based on rough set theory is adopted to extract condition information in monitoring and diagnosis for an engine,so that the technology condition monitoring parameters are optimized. The decision tables for each fault source are built and the diagnosis rules rooting in rough set reduction is applied to carry through intelligent fault diagnosis. The cases studied show that rough set method in condition monitoring and fault diagnosis can lighten the work burden in feature selection and afford advantages for autonomic learning and decision during diagnosis.

An Embedded Condition Monitoring and Fault Diagnosis System for Rotary Machines

An intelligent machine is the earnest aspiration of people. From the point of view to construct an intelligent machine with self-monitoring and self-diagnosis abilities, the technology for realizing an internet oriented embedded intelligent condition monitoring and fault diagnosis system for the rotating machine with remote monitoring, diagnosis, maintenance and upgrading functions is introduced systematically. Based on the DSP （ Digital Signal Processor） and embedded microcomputer, the system can measure and store the machine work status in real time, such as the rotating speed and vibration, etc. In the system, the DSP chip is used to do the fault signal processing and feature extraction, and the embedded microcomputer with a customized Linux operation system is used to realize the internet oriented remote software upgrading and system maintenance. Embedded fault diagnosis software based on mobile agent technology is also designed in the system, which can interconnect with the remote fault diagnosis center to realize the collaborative diagnosis. The embedded condition monitoring and fault diagnosis technology proposed in this paper will effectively improve the intelligence degree of the fault diagnosis system.

Attention mechanism based multi-scale feature extraction of bearing fault diagnosis

Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery.In practical applications,bearings often work at various rotational speeds as well as load conditions.Yet,the bearing fault diagnosis under multiple conditions is a new subject,which needs to be further explored.Therefore,a multi-scale deep belief network(DBN)method integrated with attention mechanism is proposed for the purpose of extracting the multi-scale core features from vibration signals,containing four primary steps:preprocessing of multi-scale data,feature extraction,feature fusion,and fault classification.The key novelties include multi-scale feature extraction using multi-scale DBN algorithm,and feature fusion using attention mecha-nism.The benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this method.Furthermore,the aforementioned method is compared with four classical fault diagnosis methods reported in the literature,and the comparison results show that our pro-posed method has higher diagnostic accuracy and better robustness.

A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

Most gear fault diagnosis(GFD)approaches su er from ine ciency when facing with multiple varying working conditions at the same time.In this paper,a non-negative matrix factorization(NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix,aiming to o er a fast multi-tasking solution.The short-time Fourier transform(STFT)is first used to obtain the time-frequency features from the gear vibration signal.Then,the optimal clustering numbers are estimated using the Bayesian information criterion(BIC)theory,which possesses the simultaneous assessment capability,compared with traditional validity indexes.Subsequently,the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column tasks.Finally,the parameters involved in BIC and NMF algorithms are determined using the gradient ascent(GA)strategy in order to achieve reliable diagnostic results.The Spectra Quest’s Drivetrain Dynamics Simulator gear data sets were analyzed to verify the e ectiveness of the proposed approach.

Generalized Demodulation Transform for Bearing Fault Diagnosis Under Nonstationary Conditions and Gear Noise Interferences

It is a challenging issue to detect bearing fault under nonstationary conditions and gear noise interferences. Meanwhile, the application of the traditional methods is limited by their deficiencies in the aspect of computational accuracy and e ciency, or dependence on the tachometer. Hence, a new fault diagnosis strategy is proposed to remove gear interferences and spectrum smearing phenomenon without the tachometer and angular resampling technique. In this method, the instantaneous dominant meshing multiple(IDMM) is firstly extracted from the time-frequency representation(TFR) of the raw signal, which can be used to calculate the phase functions(PF) and the frequency points(FP). Next, the resonance frequency band excited by the faulty bearing is obtained by the band-pass filter. Furthermore, based on the PFs, the generalized demodulation transform(GDT) is applied to the envelope of the filtered signal. Finally, the target bearing is diagnosed by matching the peaks in the spectra of demodulated signals with the theoretical FPs. The analysis results of simulated and experimental signal demonstrate that the proposed method is an e ective and reliable tool for bearing fault diagnosis without the tachometer and the angular resampling.

Role of Plain Abdominal X-Ray in the Differential Diagnosis of Common Acute Abdominal Conditions in Children at Olabisi Onabanjo University Teaching Hospital, Sagamu, Ogun State, Nigeria

Background: The plain abdominal x-ray is one of the commonly requested investigations in the children emergency room, paediatric surgical ward and neonatal wards. The short interval required to carry out this investigative procedure and obtain results makes it the first imaging modality used to unravel the different causes of acute abdominal conditions in children. The safety of abdominal x-ray in children makes it attractive for use in paediatric surgical practice as part of routine work-up for undifferentiated acute abdominal conditions and also to diagnose specific causes of acute abdomen in children. Setting: Olabisi Onabanjo University Teaching Hospital, Sagamu, Ogun State. Objectives: Evaluation of the role of plain abdominal x-ray in diagnosing common acute abdominal conditions in children. Materials and method: Patients admitted to the children emergency room, paediatric surgical wards, children’s ward and neonatal ward who had plain abdominal x-ray as part of their diagnostic work-up were included in the study. They were studied prospectively between March 2011 and April 2021. Results: Three Hundred and Ninety-nine patients who had plain abdominal x-rays as part of their diagnostic work-up were studied. Males were 240 while females were 159, a male to female ratio of 1.5:1. The patients were aged between 1 day to 16 years. Differential diagnoses made with plain abdominal x-ray were intestinal obstruction in 298, perforated viscus 69 patients, intra-abdominal masses 13 patients and location of intra-abdominal foreign body 14. Intestinal obstruction cases in which plain abdominal x-ray played a role in their diagnosis and management included the following: intussusception 66, neonatal sepsis 60, malrotation 48, intestinal atresia 42, anorectal malformation 32, hirschsprung’s disease in 30 cases, pyloric stenosis 24, obstructed hernia 22, post-operative adhesions 16 and intestinal helminthiasis 12. Perforated viscus accounted for 69 indications. Out of these indications, perforated gut in intussusception 19, perforated typhoid ileitis was responsible in 13 cases, gut perforation in blunt abdominal trauma 8, perforation in strangulated hernia 11 cases, perforated gut in malrotation 7, ceacal perforation in hirschsprugs disease 6 and colonic perforation in necrotizing enterocolitis 5 cases. Conclusion: Plain abdominal x-ray remains a role to play in the differential diagnosis and management of common paediatric acute abdominal conditions.

Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.

Fuzzy Condition Pattern Recognition and Fault Diagnosis for Spacecraft

This paper introduces concepts of symptom vector and fuzzy symptom vector forspacecraft condition recognition and fault diagnosis,defines an operator and suggests a fuzzy pat-tern recognition method of fault diagnosis for spacecraft.This method is verified by examples andresults are checked from an expert system.

Fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval SVM

The fault detection and diagnosis of diesel engine valve clearance can effectively improve the availability and safety of diesel engine and have extremely important value and significance.Diesel engines generally operate in various stable operating conditions,which have important influence on the fault diagnosis.However,many fault diagnosis methods have been put forward under specific stable operating condition based on vibration signal.As the result of great impact caused by operating conditions,corresponding diagnosis models cannot deal with the fault diagnosis under different operating conditions with required accuracy.In this paper,a fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval support vector machine(SVM)is proposed.Firstly,the fault features with weak condition sensitivity have been extracted according to the influence analysis of fault on vibration signal.Moreover,soft interval constraint has been applied to SVM algorithm to reduce the random influence of vibration signal on fault features.In addition,different machine learning algorithms based on different feature sets are adopted to conduct the fault diagnosis under different operating conditions for comparison.Experimental results show that the proposed method is applicable for fault diagnosis under variable operating condition with good accuracy.

Research on Key Techniques of Condition Monitoring and Fault Diagnosing Systems of Machine Groups

This paper describes the development of the condition monitoring and faultdiagnosing system of a group of rotating machinery. The data management is performed by means ofdouble redundant data bases stored simultaneously in both the analyzing server and monitoringclient. In this way, high reliability of the storage of data is guaranteed. Condensation of trenddata releases much space resource of the hard disk. Diagnosing strategies orientated to differenttypical faults of rotating machinery are developed and incorporated into the system. Experimentalverification shows that the system is suitable and effective for condition monitoring and faultdiagnosing for a rotating machine group.

STUDY ON REALISTIC TECHNOLOGY OF CONDITION MONITORING AND FAULT DIAGNOSTIC SYSTEM FOR SHIPPING POWER DEVICES

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CHARACTERISTICS AND THEIR CORRECT SELECTION IN THE FAULTS DIAGNOSIS OF HYDRAULIC

The condition characteristics of hydraulic systems reflect running condition for the hydraulic equipment directly. It is the key for condition monitoring and early fault diagnosis to select characteristics reasonably. In this paper, the types, properties of characteristics in hydraulic equipment are analysed, and some considerations in their selection are presented.

Fault Diagnosis of Mechanical Equipments Through Spectrometric oil Analysis for Worn Off Metallic Elements

In the fault prediction of mechanical equipments through spectromectric oil analysis for worn off debris, a method for the determination of the limiting value of wear is proposed and discussed. In order to diagnose the impending failure and to predict the fault modes and locate the fault spots, a comprehensive approach is studied and outlined on the basis of methods of discriminative analysis and fuzzy logic. A fault diagnosis expert system OAFDS developed by the authors for the nonitoring of working conditions of the ND5 locomotive diesel engine Nd5 is briefly introduced.

Interpretable data-driven fault diagnosis method for data centers with composite air conditioning system

Fault detection and diagnosis are essential to the air conditioning system of the data center for elevating reliability and reducing energy consumption.This study proposed a convolutional neural network(CNN)based data-driven fault detection and diagnosis model considering temporal dependency for composite air conditioning system that is capable of cooling the high heat flux in data centers.The input of fault detection and diagnosis model was an unsteady dataset generated by the experimentally validated transient mathematical model.The dataset concerned three typical faults,including refrigerant leakage,evaporator fan breakdown,and condenser fouling.Then,the CNN model was trained to construct a map between the input and system operating conditions.Further,the performance of the CNN model was validated by comparing it with the support vector machine and the neural network.Finally,the score-weighted class mapping activation method was utilized to interpret model diagnosis mechanisms and to identify key input features in various operating modes.The results demonstrated in the pump-driven heat pipe mode,the accuracy of the CNN model was 99.14%,increasing by around 8.5%compared with the other two methods.In the vapor compression mode,the accuracy of the CNN model achieved 99.9%and declined the miss rate of refrigerant leakage by at least 61%comparatively.The score-weighted class mapping activation results indicated the ambient temperature and the actuator-related parameters,such as compressor frequency in vapor compression mode and condenser fan frequency in pump-driven heat pipe mode,were essential features in system fault detection and diagnosis.

Online Condition Monitoring of Gripper Cylinder in TBM Based on EMD Method

The gripper cylinder that provides braced force for Tunnel Boring Machine （TBM） might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for the safety and efficiency of the whole tunneling project. In this paper, an online condition monitoring system based on the Empirical Mode Decomposition （EMD） method is estab- lished for fault diagnosis of the gripper cylinder while TBM is working. Firstly, the lumped mass parameter model of the gripper cylinder is established considering the influence of the variable stiffness at the rock interface, the equivalent stiffness of the oil, the seals, and the copper guide sleeve. The dynamic performance of the gripper cylinder is investigated to provide basis for its health condition evaluation. Then, the EMD method is applied to identify the characteristic frequencies of the gripper cylinder for fault diagnosis and a field test is used to verify the accuracy of the EMD method for detection of the characteristic frequencies. Furthermore, the contact stiff- ness at the interface between the barrel and the rod is calculated with Hertz theory and the relationship between the natural frequency and the stiffness varying with the health condition of the cylinder is simulated based on the dynamic model. The simulation shows that the character- istic frequencies decrease with the increasing clearance between the barrel and the rod, thus the defects could be indicated by monitoring the natural frequency. Finally, a health condition management system of the gripper cylin- der based on the vibration signal and the EMD method is established, which could ensure the safety of TBM.

A Novel Method Based on UNET for Bearing Fault Diagnosis

Reliability of rotating machines is highly dependent on the smooth rolling of bearings.Thus,it is very essential for reliable operation of rotating machines to monitor the working condition of bearings using suitable fault diagnosis and condition monitoring approach.In the recent past,Deep Learning(DL)has become applicable in condition monitoring of rotating machines owing to its performance.This paper proposes a novel bearing fault diagnosis method based on the processing and analysis of the vibration images.The proposed method is the UNET model that is a recent development in DL models.The model is applied to the 2D vibration images obtained by transforming normalized amplitudes of the time-series vibration data samples into the corresponding vibration images.The UNET model performs pixel-level feature learning using the vibration images owing to its unique architecture.The results demonstrate that the model can perform dense predictions without any loss of label information,generally caused by the sliding window labelling method.The comparative analysis with other DL models confirmed the superiority of the UNET model which has achieved maximum accuracy of 98.91%and F1-Score of 99%.

Decentralized and overall condition monitoring system for large-scale mobile and complex equipment

It is an urgent project to realize online and overall condition monitoring and timely fault diagnosis for large-scale mobile and complex equipment. Moreover, most of the existing large-scale complex equipment has quite insufficient accessibility of examination, although it still has quite a long service life. The decentralized and overall condition monitoring, as a new concept, is proposed from the point of view of the whole system. A set of complex equipment is divided into several parts in terms of concrete equipment. Every part is processed via one detecting unit, and the main detecting unit is connected with other units. The management work and communications with the remote monitoring center have been taken on by it. Consequently, the difficulty of realizing a condition monitoring system and the complexity of processing information is reduced greatly. Furthermore, excellent maintainability of the condition monitoring system is obtained because of the modularization design. Through an application example, the design and realization of the decentralized and overall condition monitoring system is introduced specifically. Some advanced technologies, such as, micro control unit （MCU）, advanced RISC machines （ARM）, and control area network （CAN）, have been adopted in the system. The system＇s applicability for the existing large-scale mobile and complex equipment is tested.

An Approach to the Fault Diagnosis and Testability in Analog Circuits at Module Level

In this paper, a module level fault diagnosis method is presented which considers multi-port device or subnetwork as the basic unit. The fault model in this method is quite similar to an actual condition,hence it has practical meaning. The equations of moedule level fault diagnosis are derived, and thetestability problem for module-fault diagnosis is discussed in general. The paper then gives severaltoplolgical conditions for module-fault testubility, which are applicable to a general nonreciprocal network by introducing a generalized independent path.

Analysis of Dynamic Condition Diagnosis of One Rare Regional Rainstorm

[Objective] The aim was to discuss dynamic conditions for one rare regional rainstorm. [Method] By using conventional material, ground encryption automatic station materials, wind profiling radar data and Doppler radar data, strong precipitation and regional large rainstorm in Lingxi area on August 3, 2010, were expounded principal of heavy weather analysis. [Result] The precipitation process was the result of different scales and different height systems influenced by the southwest airflow in the edge of subtropical high, weak cold air penetrating southward before westerly trough and the easterly in the higher layer (10 km above); the instability of atmosphere structure was the premise of strong precipitation. Ground convergent, east storm with senior northwestern current interaction triggered the release of unstable energy; the southwest airstream in the edge of subtropical high provided water vapor supply. The duration of the precipitation was short and the intensity was large. Precipitation moved to certain direction, having typical mesoscale strong convection system. Strong precipitation fell in the same place as the convergence area of wind field. The place having next strong precipitation can be predicted based on the wind field convergence position along with the movement of time. Outline radar data and Doppler radar data contour line products can more accurately represent atmospheric vertical wind field structure. [Conclusion] The study provided certain references for the report of rainstorm.