Catastrophic Tool Failure Detection Based on Acoustic Emission Signal Analysis

There is considerable interest in techniques for ensuring the quality of the laser joining process. One approach to quality monitoring is through analysis of acoustic and optical emission signals acquired during welding. Preliminary analyses confirm that these signals fluctuate chaotically, suggesting that there may be benefits to a nonlinear signal processing approach. Lyapunov exponents of cleaned signals were calculated to quantify the nonlinear nature of the process. Inspection of Lyapunov exponents calculated from optical and acoustic emission signals corresponding to a variety of weld penetration conditions revealed that no sharp distinction could be found. The Lyapunov exponents were found to depend strongly on the power and travel speed settings of the process. This variation masked any variations due solely to weld penetration. There was some distinction in Lyapunov exponents noted between non-gap and gap butt joint welds. The Lyapunov exponent for butt welds with significant joint gap was higher than for non-gapped welds. It was concluded that monitoring of weld gap might be possible using the Lyapunov exponent of the optical and/or acoustic signals.There is considerable interest in techniques for ensuring the quality of the laser joining process. One approach to quality monitoring is through analysis of acoustic and optical emission signals acquired during welding. Preliminary analyses confirm that these signals fluctuate chaotically, suggesting that there may be benefits to a nonlinear signal processing approach. Lyapunov exponents of cleaned signals were calculated to quantify the nonlinear nature of the process. Inspection of Lyapunov exponents calculated from optical and acoustic emission signals corresponding to a variety of weld penetration conditions revealed that no sharp distinction could be found. The Lyapunov exponents were found to depend strongly on the power and travel speed settings of the process. This variation masked any variations due solely to weld penetration. There was some distinction in Lyapunov exponents noted between non-gap and gap butt joint welds. The Lyapunov exponent for butt welds with significant joint gap was higher...

An improved mathematical model describing acoustic emission (AE) signals generated by different types of partial discharges (PD) that occur in electric power transformer insulation system is presented in the paper. AE signals are analyzed within the AE method as applied for power transformer failure detection due to occurrence of PD. There are several types of basic defects, which are characterized by different types of PD. The mathematical model presented here is crucial for numerical analyses and simulations, where it acts as the function describing the acoustic source in an acoustic model of power transformer insulation system. The regression procedure was performed based on empirical AE signals, registered in a laboratory experiment. The AE signals are described by a mathematical model being a multi-parameter function, which involve both the time domain and the frequency domain. Goodness of the model was evaluated based on analysis of 480 data samples in the time, frequency and time-frequency domains. Also coherence between the registered and modeled signals was calculated. It was stated that the improved model fits very well to the real data, although, due to high level of noise embodied in signals registered in experiments, the coherence values remain low. Moreover, analyses of the estimated data were performed and some example results are presented in this paper. Based on the achieved outcomes a collection of parameter values was prepared for each of the eight considered PD basic types. One can simple use it now in a numerical model for simulation of AE signal source generated by specified type of PD, what corresponds to a particular power transformer insulation system failure. Furthermore, the regression procedure presented in this paper can be easily transferred to any other types of AE sources including processes of compression, tension and cracking.

Acoustic emission signal analysis during chip formation process in high speed machining of 7050-T7451 aluminum alloy and Inconel 718 superalloy

Conducting tensile experiment with high pressure gas cylinder materials to analyze its acoustic emission (AE) signals, can realize the stretch features and AE signals regularity, which is very important to distinguish high pressure gas cylinder detection. The AE signals are divided into four different stages through the tensile experiment, which show that AE signals and theory analysis are fitted well and thus be used to distinguish materials defects.

Frequency as a key parameter in discriminating the failure types of thermal barrier coatings: Cluster analysis of acoustic emission signals

The aim of the paper is focused on the analysis of the mechanical properties of the concrete specimens with plasticizer at three point bending test by the signal analysis of the acoustic emission signal. The evaluations were compared the measurement and the results obtained with theoretical presumptions. The Joint Time Frequency Analysis applied on measurement data and its evaluation is described. It is well known that the Acoustic Emission Method is a very sensitive method to determine active cracks into structure. However, evaluation of acoustic emission signals is very difficult. A non-traditional method was used to signal analysis of burst acoustic emission signals recorded during three point bending test.

AE (acoustic emission) signals generated in laser welding were studied using wavelet time-frequency signal analysis methods. The AE signals were decomposed into a series of discrete sequences distributed over different frequency bands through wavelet transform. Further data processing was then carried out after selecting several decomposition sequences consistent with specific research goals. More detailed information of the welding process can be revealed through analyzing the time-frequency properties of the AE signals than can be obtained through statistical analysis or simple magnitude measurements. There is a significant difference between the AE signal from the desired deep-penetration weld and that which is acquired when there are problems with misalignment and excessive joint gap. The intensity of low-frequency (<781 Hz) components of the AE signals is dramatically reduced when welding defects occur. The same phenomena are found in the oscillation amplitude of frequency components between 781 and 1562 Hz. Based on the detailed information obtained from AE signals via wavelet analysis, a signal intensity moving average (IMA) curve is defined in this article. The curve is an effective tool for recognizing transitions of welding states and identifying defects quickly and reliably. The potential application of the IMA curve is also described in this article.

In the present study, the development of an acoustic emission technique (AET) based methodology is reported for online prediction of quality and shear strength of spacer pad welds of nuclear fuel pins of pressurised heavy water reactors (PHWRs). The quality evaluation of spacer pad welds was made by classification of different weld categories using cluster analysis and artificial neural network (ANN) study of acoustic emission signals generated during welding. The ANN approach was also effective in arriving at the quantitative estimation of percentage correct classification between any two classes. For assessment of shear strength of individual coins of spacer pad welds by ANN, the properties of basic sigmoidal function were exploited and this could predict the strength of each coin with an accuracy of 97%. The results assume significance because instrumentation methodology is suitable for online application and complement the currently followed statistical quality control approaches for spacer pad weld assessment.

An Acoustic Emission (AE) signal processing and analysis system is developed for process and condition monitoring of Dense Medium Cyclones (DMCs), key units in coal preparation plants. This paper describes the use of a combination of statistical signal processing, multivariate statistical analysis and pattern classification methods to formulate robust soft-sensor predictive models for DMC process variables and wear state based on passive contact AE signals. Power spectral density and statistical moment ‘feature’ variables are extracted from AE signals. Principal Component Analysis (PCA) is used to evaluate the sensitivity of the AE ‘features’ to process variables and wear state. Principal Component Regression (PCR) and Partial Least Squares (PLS) are used to quantitatively model process variables. Hierarchical clustering is used to qualitatively model wear state and process variables. The AE hardware, signal processing and soft-sensor modelling system can be used for process and condition monitoring of other mineral processing equipment.

Low temperature tensile deformation and acoustic emission signal characteristics of AISI 304LN stainless steel

Parameter analysis is a traditional and widely used method for the analysis of acoustic emission (AE) signals due to its simplicity and easy realisation. However, this approach has limitations and is not suitable for detailed analysis of the signals, and waveform-based methods are becoming attractive for the analysis of AE signals. This paper uses waveform-based analysis methods to investigate the time-frequency characteristics of AE signals arising from defects in rolling element bearings. This includes the composition of the signals, the generation mechanism of the defects and the influence of the operational condition of the bearing on these characteristics. The wavelet scalogram of the continuous wavelet transform is used to perform the qualitative analysis and the discrete wavelet transform is used for the quantitative analysis. The results demonstrate that the operational condition of the bearing will influence the time domain, the frequency domain and the time-frequency domain characteristics of the AE signals in various ways. The results also prove that the wavelet transform, and especially the wavelet scalogram, has great potential for the waveform-based analysis of AE signals from defects.

In this work the hardware-software measuring system for registration of acoustic emission (AE) signals based on personal computer (PC) for detection regions of deformation and fracture localization and their location as well as estimation of current mechanical state of loaded materials was designed. Series of experiments on measurement of basic characteristics of AE signals of the D16AT samples with a notch have been carried out under uniaxial tension. The obtained experimental dependences are agreed well with a literature data and interpreted from a position of a stage pattern of the deformation processes occurring under loading.

Lithium-ion batteries (LIBs) have become a critical technological cornerstone in modern energy infrastructure due to their high energy density and long cycle life. Monitoring of LIBs is essential for safety and reliability. Acoustic Emission (AE) technology enables non-destructive online monitoring of LIBs under normal operating conditions, coupled with electrochemical analysis. This paper employs an experimental setup to monitor AE signals during the charging and discharging processes of LIBs. A feature analysis of the preprocessed AE signals is conducted in conjunction with electrochemical-mechanical mechanisms. The results indicate that the deintercalation and intercalation of lithium ions (Li-ions) at the electrodes may generate opposite characteristics in the AE signals. The peak-peak amplitude and waveform time intervals of the signals show strong correlations with the electrical parameters and electrochemical states of the LIB under different charging and discharging modes, allowing for the identification of constant current (CC) and constant voltage (CV) stages. Analysis of data from multiple LIBs under different state demonstrates the universality of these features. This confirms that AE is an effective method for gaining detailed, real-time insights into the internal state and mechanisms of LIBs.

A study of tool wear using statistical analysis of metal-cutting acoustic emission

Wear mechanism of a cutting tool is highly complex in that the processes of tool wear results from interacting effect of machining configurations. Various output generated by the study and analysis of each tool is extremely useful in analyzing the tool characteristics in general and to make efforts to obtain the estimated tool life in particular. The gradual process of tool wear has adverse influence on the quality of the surface generated and on the design specifications in the work piece dimensions and geometry, and causes, at the worst case, machine breakdown. Advanced manufacturing demands proper use of the right tool and emphasizes the need to check the wear rate. A scientific method of obtaining conditions for an optimal machining process with proper tools and control of machining parameters is essential in the present day manufacturing processes. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the indirect methods of tool wear analysis and monitoring is based on the acoustic emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process and provides a means of evaluating the wear of cutting tools. Wear parameters obtained in the process are analyzed with the output generated by using Multi Layer Perceptron (MLP) based back propagation technique and Adaptive Neuro Fuzzy Interference System (ANFIS). The results obtained from these methods are correlated for the actual and predicted wear. Experiments have been conducted on EN8 and, EN24 using Uncoated Carbide, Coated carbide and Ceramic inserts (Kennametal, India make) on a high speed lathe for the most appropriate cutting conditions. The AE signal analysis (considering signal parameters such as, ring down count (RDC), rise time (RTT), event duration (ED) and energy (EG). Flank wear in tools and corresponding cutting forces for each of the trials are measured and are correlated for various combinations of tools and materials of work piece.