Non Destructive Defect Detection by Spectral Density Analysis

Abstract

The potential nondestructive diagnostics of solid objects is discussed in this article. The whole process is accomplished by consecutive steps involving software analysis of the vibration power spectrum (eventually acoustic emissions) created during the normal operation of the diagnosed device or under unexpected situations. Another option is to create an artificial pulse, which can help us to determine the actual state of the diagnosed device. The main idea of this method is based on the analysis of the current power spectrum density of the received signal and its postprocessing in the Matlab environment with a following sample comparison in the Statistica software environment. The last step, which is comparison of samples, is the most important, because it is possible to determine the status of the examined object at a given time. Nowadays samples are compared only visually, but this method can’t produce good results. Further the presented filter can choose relevant data from a huge group of data, which originate from applying FFT (Fast Fourier Transform). On the other hand, using this approach they can be subjected to analysis with the assistance of a neural network. If correct and high-quality starting data are provided to the initial network, we are able to analyze other samples and state in which condition a certain object is. The success rate of this approximation, based on our testing of the solution, is now 85.7%. With further improvement of the filter, it could be even greater. Finally it is possible to detect defective conditions or upcoming limiting states of examined objects/materials by using only one device which contains HW and SW parts. This kind of detection can provide significant financial savings in certain cases (such as continuous casting of iron where it could save hundreds of thousands of USD).

Modern Sensor Technology for Alphanumeric Recognition in Metallurgy Industry

“…However, the developed program on PC or embedded control system is possible by SDK communication libraries. The innovation of embossed alphanumeric marks recognition sensor methods is very needful especially for marking machine in metallurgy industry because of dust faults [2].…”

Prediction and Evaluation of the Molds State Using the Tools of Articicial Intelligence

“…2. From the picture is obvious, that system design contain several basic functions, which aim to achieve previously mentioned goals, which are [1,[10][11][12][13][14][15]:…”

“…5 is the user interface for prediction of diagnostics and samples of defects on mold's plates. [4,12,13] Evaluation of the state and prediction using of model of the Weibull of material Fig. 4 degradation module screen and examples of models of material degradation…”

“…These parameters were used as inputs and were analyzed. [9,[11][12][13][14] The aim of these analyzes was to determine the parameters that affect the wear of the crystallizer. As an output parameter was the probability of achieving the ultimate limit state, assuming Weibull distribution.…”

A new method for automatic defects detection and diagnosis in rolling element bearings using Wald test

“…Visibly noticed are revolution methods based on mechanical signal processing, which are divided into two main categories, detection and diagnosis, and are based on time-frequency methods and temporal methods or a combination of both. Thus, many methods are born, the scalar indicators such as kurtosis, skew, crest factor (Dron et al, 2004;Pachaud et al, 1997), demodulation and detection of the envelope (Sheen, 2004(Sheen, , 2008, amplitude modulation (Stack et al, 2004), detection of vibration modes (Rizos et al, 1990), de-noising vibratory signals , the spectral density analysis (Krejcar and Frischer, 2011), the Fast Fourier Transform (Lenort, 1995) (Bendjama and Boucherit, 2016), blind source separation (Wang et al, 2014), fuzzy logic (Liu et al, 1996). El-Thalji and Jantunen (2015) and Rai and Upadhyay (2016) reviewed almost all the techniques used in the domain predicting defects.…”