Chromatic monitoring of gear mechanical degradation based on acoustic emission

This paper presents a methodology for the feature estimation of a new fault indicator focused on detecting gear mechanical degradation under different operating conditions. Preprocessing of acoustic emission signal is performed by applying chromatic transformation to highlight characteristic patterns of the mechanical degradation. In this study, chromaticity based on the computation of the hue, light, and saturation transformation of the main acoustic emission intrinsic mode functions is performed. Then, a topology preservation approach is carried out to describe the chromatic signature of the healthy gear condition. Thus, the detection index can be estimated. It must be noted that the applied chromatic monitoring process only requires the characterization of the healthy gear condition, being applicable to a wide range of operating conditions of the gear. Performance of the proposed system is validated experimentally. According to the obtained results, the proposed methodology is reliable and feasible for monitoring gear mechanical degradation in industrial applications.Peer ReviewedPostprint (published version

Prediction, classification and diagnosis of spur gear conditions using artificial neural network and acoustic emission

The gear system is a critical component in the machinery and predicting the performance of a gear system is an important function. Unpredictable failures of a gear system can cause serious threats to human life, and have large scale economic effects. It is necessary to inspect gear teeth periodically to identify crack propagation and, other damages at the earliest. This study has two main objectives. Firstly, the research predicted and classified specific film thickness (λ) of spur gear by Artificial Neural Network (ANN) and Regression models. Parameters such as acoustic emission (AE), temperature and specific film thickness (λ) data were extracted from works of other researchers. The acoustic emission signals and temperature were used as input to ANN and Regression models, while (λ) was the output of the models. Second objective is to use the third generation ANN (Spiking Neural Network) for fault diagnosis and classification of spur gear based on AE signal. For this purpose, a test rig was built with several gear faults. The AE signal was processed through preprocessing, features extraction and selection methods before the developed ANN diagnosis and classification model were built. These processes were meant to improve the accuracy of diagnosis system based on information or features fed into the model. This research investigated the possibility of improving accuracy of spur gear condition monitoring and fault diagnoses by using Feed-Forward Back- Propagation Neural Networks (FFBP), Elman Network (EN), Regression Model and Spiking Neural Network (SNN). The findings showed that use of specific film thickness has resulted in the FFBP network being able to provide 99.9% classification accuracy, while regression and multiple regression models attained 73.3 % and 81.2% classification accuracy respectively. For gear fault diagnosis, the SNN achieved nearly 97% accuracy in its diagnosis. Finally, the methods use in the study have proven to have high accuracy and can be used as tools for prediction, classification and fault diagnosis in spur gear

Two-dimensional analysis for implementing nondestructive crack detection system in automotive production line

Department Of Electrical EngineeringAccording to the automotive market trend, the vehicle machine needs to the electronic components. In the automotive body panel, appearance of cracks is one of the most serious challenge. Sometimes surface cracking will cause an unexpired expenses in production process. The most common method used to detect the crack in automotive press line is a visual scanning of objects, parts or components which is the oldest and reliable non-destructive testing method. This test method is applied to almost every automotive product as a quality assurance.However, the specific optimization method with great accuracy the time and effort is required for high speed throughput in an automated press line system. An acoustic emission is a technique which centered on the concept of utilizing the transducer action of a flaw in a stressfield. This technique was used to investigate fatigue crack characteristics such as initiation closure and propagation on smooth specimens. It is shown that acoustic emission from unflawedtensile specimens can be treated from a dislocation dynamics approach. The choice of analytical method is extremely important and should not only focus on high-accuracy crack detection, but should also low-cost with high-efficiency in this system. In cases where crack is expected or necessary, the analytical method should detect the acoustic emission signal while the accuracy of the resulting measurements should fall within an acceptable range. The purpose of the detection system is acquisition correctly and to verify accuracy of the measurements. When the accuracy of crack detection falls out of predetermined acceptability criteria, usually within 20% accuracy, the measured data should be reanalyzed by using other methods, if necessary. The system consists of two parts: the hardware and the DSP (digital signal processing) part which includes AE parameter analyzer, based on the LabVIEW program. The crack acquisition system is set to sampling rate of 300 KHz with 20dB pre???amplification. As a result, maximum received frequency range is 150 kHz according to the field test. Operating temperature is -40??C ~ +85??C considering the severe press factory environment with 7 seconds to analyze the data. The proposed system was tested and successfully demonstrated crack detection in an actual automotive production line.ope

Model Referenced Condition Monitoring of High Performance CNC Machine Tools

Generally, machine tool monitoring is the prediction of the system’s health based on signal acquisition and processing and classification in order to identify the causes of the problem. The producers of machine tools need to pay more attention to their products life cycle because their customers increasingly focus on machine tool reliability and costs. The present study is concerned with the development of a condition monitoring system for high speed Computer Numerical Control (CNC) milling machine tools. A model is a simplification of a real machine to visualize the dynamics of a mechatronic system. This thesis applies recent modelling techniques to represent all parameters which affect the accuracy of a component produced automatically. The control can achieve an accuracy approaching the tolerance restrictions imposed by the machine tool axis repeatability and its operating environment. The motion control system of the CNC machine tool is described and the elements, which compose the axis drives including both the electrical components and the mechanical ones, are analysed and modelled. SIMULINK models have been developed to represent the majority of the dynamic behaviour of the feed drives from the actual CNC machine tool. Various values for the position controller and the load torque have been applied to the motor to show their behaviour. Development of a mechatronic hybrid model for five-axis CNC machine tool using Multi-Body-System (MBS) simulation approach is described. Analysis of CNC machine tool performance under non-cutting conditions is developed. ServoTrace data have been used to validate the Multi-body simulation of tool-to-workpiece position. This thesis aspects the application of state of art sensing methods in the field of condition monitoring of electromechanical systems. The ballscrew-with-nut is perhaps the most prevalent CNC machine subsystem and the condition of each element is crucial to the success of a machining operation. It’s essential to know of the health status of ballscrew, bearings and nut. Acoustic emission analysis of machines has been carried out to determine the deterioration of the ballscrew. Standard practices such as use of a Laser Interferometer have been used to determine the position of the machine tool. A novel machine feed drive condition monitoring system using acoustic emission (AE) signals has been proposed. The AE monitoring techniques investigated can be categorised into traditional AE parameters of energy, event duration and peak amplitude. These events are selected and normalised to estimate remaining life of the machine. This method is shown to be successfully applied for the ballscrew subsystem of an industrial high-speed milling machine. Finally, the successful outcome of the project will contribute to machine tool industry making possible manufacturing of more accurate products with lower costs in shorter time