Mohd Noor Baharin

On the Need of Kurtosis-Based Technique to Evaluate the Fatigue Life of a Coil Spring

Automotive suspension systems have big potential of mechanical failure due to fatigue as this system was subjected to the variable amplitude loading under service condition. This study was specifically focuses on coil spring, one of the suspension system parts. When vehicle was driven on any road surfaces and hit potholes or bump, this coil spring is then affected by the significant load. This significant load will cause the damage to this component. The objective of this study to predict the coil spring fatigue damage and then relate it with the I-kaz coefficient, results from a new statistical-based method, I-kaz method. The signal captured from this repeated loading was known as fatigue signal. The car was driven on three different roads, highway, country road and damage-surface road. The strain gauges were mounted on the outer surfaces of the coil spring to capture the fatigue load variable amplitude data on the road. The recorded strain was then analysed for fatigue damage and I-kaz coefficient purposes. From the analysis, it was found that the damage road gives the highest fatigue damage rather than the other road and also the fatigue damage was proportionally related to the I-kaz coefficient.

Comparative Study on Data Editing Techniques for Fatigue Time Series Signals

This paper presents the comparative study on three types of fatigue data editing technique for summarising long records of fatigue data. Two of techniques were developed based on timefrequency domain (continous wavelet and discrete wavelet) and another one technique was developed based on time domain. These three techniques are used to extract fatigue damaging events in the record that cause the majority of fatigue damage, whilst preserving the load cycle sequence in the data. The objective of this study is to observe the capability of each technique in summarising long records of fatigue data. For the purpose of this study, two set of nonstationary data that exhibits random behaviour was used. This random data was measured in microstrain unit on the SAE1045 material that were used as a lower suspension arm of a car. Experimentally, the data was collected for 60 seconds at sampling rate of 500Hz, which gave 30, 000 discrete data points. The result of the study indicates that all techniques are applicable in detecting and extracts fatigue damaging events that exist in the fatigue data. However, it was found that continous wavelet can extract the data better than the other technique based the shorten signals, retention of damage and statistical parameter.

A Weighted Genetic Algorithm Based Method for Clustering of Heteroscaled Datasets

This paper introduces a weighted genetic algorithm (GA) based clustering method for datasets with differently scaled dimensions. Several types of synthetic two dimensional scatter data were clustered using the typical k-means clustering method. The weighted GA-based clustering method was developed to address the problem of clustering data with differently scaled (heteroscaled) dimensions. Cluster analysis results obtained from using this method was compared to the results produced from the application of the traditional k-means clustering. By introducing weights in the fitness evaluation component of the meta-heuristic search method, a more efficient clustering of heteroscaled data was produced. In real applications, this method can be used in cluster analyses of scatter data with significantly different scales in dimensions, such as kurtosis versus fatigue damage relationship scatter data.

The identification of low fatigue damage using fuzzy double clustering framework

Identifying damaging or non-damaging events in long records of fatigue data is a crux of recapitulating pristine data. In this article, a fuzzy double clustering framework (DCf) is utilized to classify the fatigue segment by exploiting two typical statistical features; kurtosis and the standard deviation. In the first stage, segments are assigned to a number of similar groups to generate multi-dimensional prototypes. Then, the resulting multi-dimensional prototypes are projected onto each featuring space of the input variables. On each dimension, a hierarchical clustering is applied to extract the information granules. For ease of interpretability, the granules are translated into a set of antecedent-consequent rules by means of a fuzzy set theory where for the model output, two distinct classes namely low and high with different degrees of evidence are assigned. The results reveal that the fatigue segments could be classified according to the value of kurtosis and standard deviation in a specific range where further, it can be a part of a fatigue data editing process

A Study on Validation of Fatigue Damage Clustering Analysis Technique Based on Clustering Validation Index

This paper presents the comparative study on two types of the clustering technique for decomposing Variable Amplitude (VA) loadings signals based on its amplitude. These two techniques are used to recognize clusters or patterns of fatigue damaging events in the record which will bring aboutthe majority of fatigue damage. However, one of the problems that existswhencomparing which technique will produce better clusters is the fact thata clustering validation index isneeded. In this study, techniques that were used were theFuzzy C-means and C-means. At first, the VA data weresegmented using the Running Damage Extraction (RDE) technique. Then, each segment produced wasanalysed using the strain life approach and global statistical signal values. Finally, the accuracy of each clustering technique wasmeasured based on the OV coefficient index. From the study, the index shows that the Fuzzy C-means technique produced much better clusters rather than the C-mean clustering technique.

Optimising Real-Time Performance of Genetic Algorithm Clustering Method

This paper presents the optimisation of real-time performance of the genetic algorithm clustering method. This performance optimisation concerns the population diversity and limitation and is based on actual runtime of the algorithm. A real-time ticker is incorporated into the algorithm for actual runtime measurement. For population diversity and limitation, a controlled k-means analysis is performed on the population of solutions to determine its diversity. Achieving a less diverse population in less amount of time without sacrificing the accuracy of the algorithm will help reduce the time-complexity of the algorithm, thus opening up the potential for the algorithm to cluster data in higher dimensions. Results from this study will be used for improving the method of clustering fatigue damage features of automotive components using genetic algorithm based methods.

Variable Amplitude Loading Strains Data Distribution Using Probability Density Function and Power Spectral Density

This paper presents a relative study on variable amplitude (VA) strain data distribution using the approach of probability density function (PDF) and power spectral density (PSD). PDF is a technique to identify the probability of the value falling within a particular interval, and a PSD is to measure the power of a signal by converting it from the time domain to the frequency domain. The objective of this study is to observe the applicability of both techniques in detecting the pattern behaviour in terms of energy and probability distribution. For this reason, a set of case study data consist of nonstationary VA pattern with a random behaviour was used. This kind of data was measured by fixing a strain gauge that connected to the strain data acquisition on the lower suspension arm of a mid-sized sedan car. The data was measured for 60 seconds at the sampling rate of 500 Hz, which gave 30,000 discrete points. The distribution of collected data was then calculated and analysed in the form of both PDF and PSD, and they were then compared for further analysis. The findings from this study are expected for determining the pattern behaviour that exists in VA strain signals.

The Development of Validation Technique in Variable Amplitude Loadings Strain Repetitive Data Collection

In any experiment, there is a need to verify the reliability of the collected data. One of the solutions is to measure the data repetitively. It will lead to measurement of inconsistency that exists in the data. Similar things also happen in variable amplitude (VA) loading strain data collection where the data also need to be measured repetitively in order the collected data is reliable. The main objective of this study is to validate the reliability of collected VA loading strain data. Two techniques will be used for identifying the similarity in the pattern for strain signal which was measured repetitively. The probability distribution function (PDF) and power spectral density (PSD) diagrams of the strain data were used as the main tool to construct profile plots for the case study data. Then, each profile plot will be compared to each other in order to identify any similarities that exist in the case study data. For the purpose of the study, a set of nonstationary VA loadings strain data that exhibits random behaviour was used. This random data was measured in the unit of microstrain on the lower suspension arm of a car. The data was repetitively measured for 60 seconds at the sampling rate of 500Hz, giving 30,000 discrete data points. The distribution of the collected data was analysed using the PDF and PSD. Based on the analysis, it was found that PSD can produce better results at identifying the similar features that exist in the profile plot compared to PDF.