Health Monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms

In-process damage to a cutting tool degrades the surface􀀀nish of the job shaped by machining and causes a signi􀀀cant􀀀nancial loss.This stimulates the need for Tool Condition Monitoring(TCM)to assist detection of failure before it extends to the worse phase.Machine Learning(ML)based TCM has been extensively explored in the last decade.However,most of the research is now directed toward Deep Learning(DL).The“Deep”formulation,hierarchical compositionality,distributed representation and end-to-end learning of Neural Nets need to be explored to create a generalized TCM framework to perform eciently in a high-noise environment of cross-domain machining.With this motivation,the design of dierent CNN(Convolutional Neural Network)architectures such as AlexNet,ResNet-50,LeNet-5,and VGG-16 is presented in this paper.Real-time spindle vibrations corresponding to healthy and various faulty con􀀀gurations of milling cutter were acquired.This data was transformed into the time-frequency domain and further processed by proposed architectures in graphical form,i.e.,spectrogram.The model is trained,tested,and validated considering dierent datasets and showcased promising results.

Wireless Self-Powered Vibration Sensor System for Intelligent Spindle Monitoring

In recent years,high-end equipment is widely used in industry and the accuracy requirements of the equipment have been risen year by year.During the machining process,the high-end equipment failure may have a great impact on the product quality.It is necessary to monitor the status of equipment and to predict fault diagnosis.At present,most of the condition monitoring devices for mechanical equipment have problems of large size,low precision and low energy utilization.A wireless self-powered intelligent spindle vibration acceleration sensor system based on piezoelectric energy harvesting is proposed.Based on rotor sensing technology,a sensor is made to mount on the tool holder and build the related circuit.Firstly,the energy management module collects the mechanical energy in the environment and converts the piezoelectric vibration energy into electric energy to provide 3.3 Vfor the subsequent circuit.The lithium battery supplies the system with additional power and monitors’the power of the energy storage circuit in real-time.Secondly,a three-axis acceleration sensor is used to collect,analyze and filter a series of signal processing operations of the vibration signal in the environment.The signal is sent to the upper computer by wireless transmission.The host computer outputs the corresponding X,Y,and Z channel waveforms and data under the condition of the spindle speed of 50∼2500 r/min with real-time monitoring.The KEIL5 platform is used to develop the system software.The small-size piezoelectric vibration sensor with high-speed,high-energy utilization,high accuracy,and easy installation is used for spindle monitoring.The experiment results show that the sensor system is available and practical.

Structural Health Monitoring by Accelerometric Data of a Continuously Monitored Structure with Induced Damages

The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.

Industrial LAN for Vibration Monitoring and Fault Diagnosis of Turbo-Generator

The unique of using industrial LAN based on field bus to construct the system of vibration monitoring and fault diagnosis is introduced. The LAN topology, client/server architecture, database and designing of application software for vibration monitoring and fault diagnosis are involved. How to apply industrial LAN to the vibration monitoring and fault diagnosis of turbo generator is discussed, and a scheme of how to construct the industrial LAN for vibration monitoring and fault diagnosis of turbo generator is presented.

Fault diagnosis and analysis of main sea water pump based on vibration monitoring in offshore oil field

The main sea water pump is the key equipment for the floating production storage and offloading （FPSO）. Affected by some factors such as hull deformation, sea water corrosion, rigid base and pipeline stress, the vibration value of main sea water pump in the horizontal direction is abnormally high and malfunctions usually happen. Therefore, it is essential to make fault diagnosis of main sea water pump, By conventional off-line monitoring and vibration amplitude spectrum analysis, the fault cycle is found and the alarm value and stop value of equipment are set, which is helpful to equipment maintenance and accident prevention.

Monitoring and Assessing Method for Blasting Vibration on Open-pit Slope in Hainan Iron Mine

A type of velocity sensor CD 1, an auto recording instrument on blasting vibration YBJ 1 and a random signal and vibration analysis system (CRAS) were used to monitor the particle vibration induced by blasting at open pit slope in Hainan Iron Mine. The attenuating rules of blasting ground vibration on slope were developed. By means of the analysis and calculation of the blasting vibration data at open pit slope and the vertical particle vibration velocity assessment method based on the concept of vibration strength, the empirical attenuating equations which can be used for predicting and estimating the damage of slope were derived.

Variational Mode Decomposition for Rotating Machinery Condition Monitoring Using Vibration Signals

The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process.Variational mode decomposition(VMD)is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions(VMFs)adaptively and non-recursively.The VMD method offers improved performance for the condition monitoring of rotating machinery applications.However,determining an accurate number of modes for the VMD method is still considered an open research problem.Therefore,a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF.Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method.The statistical parameters of the signals are extracted from the original signals,VMFs and intrinsic mode functions(IMFs)and have been fed into machine learning algorithms to validate the performance of the VMD method.The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery.Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications.

Field monitoring of railroad embankment vibration responses in seasonally frozen regions

To investigate the vibration characteristics of a railway subgrade in different seasons, three field experiments were carried out in the seasonally frozen Daqing area of China during spring, smnmer, and winter. The vibration characteristics and attenuation rates of the subgrade induced by passing trains were investigated, and the influences of the season, train speed, train type, train load, and number of train compartments are described in this paper. The results show that： （1） near the rail track the vibration in the vertical direction was more significant than in the lateral and longitudinal directions, and as the distance from the railway track increased, the acceleration amplitudes and the attenuation rates all decreased in all three directions; （2） the acceleration amplitudes and at- tenuation rates decreased in the three different study seasons as the distance from the railway track increased, and the attenuation rates in the freezing period were the largest; and （3） the acceleration amplitude induced by a freight train was greater than that by a passenger train, and the subgrade vibration increased with increasing passenger train speeds when the number of train compart- ments was similar. These results have great significance for enhanced understanding of the characteristics of wain-induced vibra- tion embankment response in seasonally frozen regions, and provide essential field monitoring data on train-induced vibrations in order to improve the performance criteria of railroading in seasonally frozen regions.

Vibration Deformation Monitoring of Offshore Wind Turbines Based on GBIR

In view of the disadvantages of vibration safety monitoring technology for offshore wind turbines,a new method is proposed to obtain deformation information of towering and dynamic targets in real-time by the ground-based interferometric ra-dar(GBIR).First,the working principle and unique advantages of the GBIR system are introduced.Second,the offshore wind turbines in Rongcheng,Shandong Province are selected as the monitoring objects for vibration safety monitoring,and the GPRI-II portable radar interferometer is used for the health diagnosis of these wind turbines.Finally,the interpretation method and key processing flow of data acquisition are described in detail.This experiment shows that the GBIR system can accurately identify the millimeter-scale vibration deformation of offshore wind turbines and can quickly obtain overall time series deformation images of the target bodies,which demonstrate the high-precision deformation monitoring ability of the GBIR technology.The accuracy meets the requirements of wind turbine vibration monitoring,and the method is an effective spatial deformation monitoring means for high-rise and dynamic targets.This study is beneficial for the further enrichment and improvement of the technical system of wind turbine vibration safety monitoring in China.It also provides data and technical support for offshore power engineering management and control,health diagnosis,and disaster prevention and mitigation.

Sensor Layout of Hoisting Machinery Vibration Monitoring Based on Harmony Genetic Search Algorithm

With the construction of automated docks,health monitoring technology as a parallel safety assurance technology for unmanned hoisting machinery has become a hot spot in the development of the industry.Hoisting machinery has a huge structure and numerous welded joints.The complexity and nonlinearity of the welded structure itself makes the structural failure parts random and difficult to arrange for monitoring sensors.In order to solve the problem of effectiveness and stability of the sensor arrangement method for monitoring the structure of hoisting machinery.Using the global and local search capabilities enhanced by the complementary search mechanism,a structural vibration monitoring sensor placement algorithm based on the harmony genetic algorithm is proposed.Firstly,the model is established for modal analysis to obtain the displacement matrix of each mode.Secondly,the optimal parameter combination is established through parameter comparison,and the random search mechanism is used to quickly search in the modal matrix to obtain the preliminary solution,and then the preliminary solution is genetically summed The mutation operation obtains the optimized solution,and the optimal solution is retained through repeated iterations to realize the decision of the vibration sensor layout of the crane structure monitoring.Combining the comparison test of harmony genetic algorithm,harmony search algorithm and genet-ic algorithm,the fitness of harmony genetic algorithm in X,Y and Z directions were 0.0045,0.0084 and 0.0058,respectively,which were all optimal.And the average probability of deviating from the optimal path is 1.10%,19.34%,and 54.43%,which are also optimal.Harmony genetic algorithm has the advantages of simplicity,fastness and strong global search ability,and can obtain better fitness value and better search stability.

Vibration Signals and Condition Monitoring for Wind Turbines

Rolling element bearings are critical parts of modern wind turbines as they carry the loads of the turning structure and the wind force. The stochastic nature of the wind loads makes it difficult to estimate the useful operational life of the bearings. Condition monitoring of these bearings in a real time environment could be very helpful in estimating their performance and in scheduling maintenance actions when a condition-based maintenance strategy is followed. This procedure can be successfully implemented by using vibration analysis in the time domain or in the frequency domain, giving useful results about the current condition of bearings and the location of potential faults. Permanently located transducers on proper positions on the bearings’ housings can be used in order to collect, process and evaluate real time measurements and provide information about the bearing’s performance. In this work, a test rig is utilized in order to evaluate the performance of rolling bearings. The results of the experimentation are satisfactory and the progress of fatigue failures can be predicted through vibration analysis techniques showing that implementation in real scale may be useful.

Monitoring and Detection of Wind Turbine Vibration with KNN-Algorithm

Maintenance for wind turbines has been transformed using supervised machine learning techniques. This method of automatic and autonomous learning can identify, monitor, and detect electrical and mechanical components of wind turbines and predict, detect, and anticipate their degeneration. Using a machine learning classifier and frequency analysis, we simulate two failure states caused by bearing vibrations. Implementing KNN facilitates efficient monitoring, monitoring, and fault-finding for wind turbines. It is possible to reduce downtime, anticipate breakdowns, and import offshore aspects through these technologies.

Research on the Correlation Between Oil Menitoring and Vibration Monitoring in Information Collecting and Processing Monitoring

Oil monitoring and vibration monitoring are two principal techniques for mechanical fault diagnosis and condition monitoring at present. They monitor the mechanical condition by different approaches, nevertheless, oil and vibration monitoring are related in information collecting and processing. In the same mechanical system, the information obtained from the same information source can be described with the same expression form. The expressions are constituted of a structure matrix, a relative matrix and a system matrix. For oil and vibration monitoring, the information source is correlation and the collection is independent and complementary. And oil monitoring and vibration monitoring have the same process method when they yield their information. This research has provided a reasonable and useful approach to combine oil monitoring and vibration monitoring.

Design on Vibration Monitoring and Fault Diagnosis System of Large Water Pump Motor

Large water pump motor,whose operation decides the reliability of the whole production line,plays a very important role.Therefore,its online condition monitoring can help companies better know its operation,process fault analysis and protection.The essay mainly studies and designs large water pump motor′s real time vibration monitoring and fault diagnosis system.The essay completes the systems project design,the establishment of the system and performance test.Eddy-currentsensor,XM-120 vibration module,XM-320 axial translation module,XM-362 temperature module,XM-360 process amount module and XM-500 gateway module are used to measure the axial vibration and displacement of main motors.Laboratory tests prove that the system can meet the requirements of motor vibration monitoring.

Vibration Severity Monitoring and Evaluation of Armored Vehicle Transmission

Vibration monitoring and vibration severity evaluation of armored vehicle transmission are realized by additional sensors. An algorithm of vibration severity in frequency domain is presented. The algorithm has powerful applicability for signal type and flexible selectivity for frequency range,and avoids the processing of signal conversion used calculus and filtering compared to the algorithm of vibration severity in time domain. An applied example is given in company with attentive proceedings and measures for improving evaluation effect.

Monitoring the Coldhead of Magnetic Resonance Imaging Systems by Means of Vibration Analysis

Magnetic resonance imaging (MRI) systems require a cooling close to the absolute zero point. This is necessary to avoid thermal losses due to the extremely high currents in the coils of the electromagnet used to generate the static magnetic field. The cooling is usually achieved using helium based refrigerating machine. The coldhead is an important and critical mechanical component in this system. An inefficient or failed coldhead can lead to severe damages to the MRI system or to the loss of helium. Hence, a continuous and reliable monitoring of this system component is necessary but not always available. To tackle this problem, we propose a monitoring system by means of analyzing the structure-borne noises caused by the mechanical activities of the coldhead. For this purpose, a measurement system based on piezoelectric elements was designed and implemented. Vibrations were measured at various locations at the MRI scanner with and without MR imaging. In all positions, the function of the coldhead could be detected. Hence, the developed system is suitable for monitoring an MRI’s coldhead without directly accessing the MR scanner’s hardware or software. For a future long-term monitoring, the aim is to predict a failure of the MRI’s coldhead based on changes in the vibrations signals.

A Study on Health Monitoring of Structural Damages for Two Stories Model by Using Vibration Test

Many structures in Japan were built after the war at a revival term or rapid economic growth. These structures have been reached a life in recent years and it is economically not affordable to conduct repair and reconstruct these structures only with a possibility of being damaged. This paper presents an approach to detect the structural damages for two degrees of freedom (2DOF) model. In this study, we conducted Microtremor measurement, free vibration test and vibration test. The 2DOF model was demonstrated the feasibility of using the proposed approach to damage detection of structural member.

SVM-Algorithm for Supervision, Monitoring and Detection Vibration in Wind Turbines

With the implementation of supervised machine learning techniques, wind turbine maintenance has been transformed. A wind turbine’s electrical and mechanical components can be automatically identified, monitored, and detected to predict, detect, and anticipate their degeneration using this method of automatic and autonomous learning. Two different failure states are simulated due to bearing vibrations and compared with machine learning classifier and frequency analysis. A wind turbine can be monitored, monitored, and faulted efficiently by implementing SVM. With these technologies, downtime can be reduced, breakdowns can be anticipated, and aspects can be imported if they are offshore.

Fast Tool for Structural Monitoring of a Pier After Impact of a Very Large Vessel Using Ambient Vibration Analysis

Operational modal analysis is a non-destructive structural investigation that considers only the loads resulting from service conditions.This approach allows the measurement of vibrations on a given structure with no need to interrupt its use.The present work aims to develop a numerical model to represent the global structural behavior of a vessel breasting dolphin using a technique that is simple and cheap in order to obtain a fast answer about the stiffness of a pier after the collision of ships with capacity up to 400,000 t.To determine the modes of vibration,one accelerometer was installed on the breasting dolphin located on the pier and a frequency domain technic was conducted over recorded data to obtain modal parameters of the structure.In situ measurements were compared to data from a finite element model based on the original structural design in order to adapt the model to accurately represent the actual behavior of the system.This allowed a reliable structural analysis that accounted for existing structural damage and imperfections.The results of the experiment presented herein are the numerical characterization of the structure,along with the structural analysis to assess the degree of damage currently observed on the system.It is noted that the dolphin subjected to ship impacts presents a reduction in stiffness of approximately10%and its global damage level can be monitored from now after new accidents.

Design of Vibration Monitoring System of CNC Machine

CNC machine have a fast development and is widely used in China. Generally, CNC machine tool, includs CNC lathes and CNC milling machine. CNC machine tool is a necessary tool for machining. It plays an important role in the mechanical design and machining fields. CNC machine tool is mainly composed of two parts of the machine body and the computer control system. Mechanical equipment failures usually related information such as vibration, sound, pressure, temperature performance. CNC machine tool vibration monitoring system with piezoelectric accelerometer, the eddy current displacement sensor, signal amplifier, signal conditioning modules. We can take an advantage of the CNC machine tool vibration monitoring system for vibration monitoring and fault diagnosis of CNC machine tools.