Fatigue Strain Signals Research Articles

Fatigue strain signal reconstruction technique based on selected wavelet decomposition levels of an automobile coil spring

This study aims to assess the fatigue life analysis of strain signals with the implementation of a reconstruction of the signals of the discrete wavelet transform (DWT) decomposition technique. The process involves the time and frequency information used in fatigue life analysis. Strain signals, also known as original signals, were measured based on different road conditions. The DWT method was applied to the original signals to assess the time–frequency domain characteristics. As a result, 14 decomposition signals were obtained and the fatigue life of each signal was evaluated. The decomposition signals contained lower fatigue life, which is below 108 cycles determined at levels 6 to 9. The reconstructed signals were obtained based on these selected levels. The frequency information obtained from the power spectral density (PSD) shows the elimination of high frequency, with low PSD amplitude in the reconstructed signal. The frequency range with high PSD amplitude obtained from this reconstructed signal at 20 Hz was the same as the original signal. Good fatigue life correlation between the reconstructed and original signals enable the prediction of similar fatigue life values, which are at 107 and 105 cycles for the highway and bumpy road conditions, respectively. It can be concluded that the implementation of this technique was able to provide good agreement of strain signals in fatigue life analysis.

Probabilistic-Based Analysis for Damaging Features of Fatigue Strain Loadings

This paper presents the behaviour of fatigue damage extraction in fatigue strain histories of automotive components using the probabilistic approach. This is a consideration for the evaluation of fatigue damage extraction in automotive components under service loading that is vital in a reliability analysis. For the purpose of research work, two strain signals data are collected from a car coil spring during a road test. The fatigue strain signals are then extracted using the wavelet transform in order to extract the high amplitude segments that contribute to the fatigue damage. At this stage, the low amplitude segments are removed because of their minimal contribution to the fatigue damage. The fatigue damage based on all extracted segments is calculated using some significant strain-life models. Subsequently, the statistics-based Weibull distribution is applied to evaluate the fatigue damage extraction. It has been found that about 70% of the probability of failure occurs in the 1.0 x 10-5 to 1.0 x 10-4 damage range for both signals, while 90% of the probability of failure occurs in the 1.0 x 10-4 to 1.0 x 10-3 damage range. Lastly, it is suggested that the fatigue damage can be determined by the Weibull distribution analysis

Characterization of Wavelet Decomposition Strain Signal Using the K-Mean Clustering Method

This paper aims to study the characterisation of time-frequency domain to analyse the fatigue strain signal due to weaknesses in time domain and frequency domain approaches. The objectives were to determine the behaviour of strain signal, characterise the fatigue life of strain signal and validate the fatigue life in time-frequency domain. The strain signal was obtained using data acquisition devices and strain gauges on two types of road condition including highway and industrial area. The acquired signals were analysed with time domain, frequency domain and time-frequency domain approaches. In time-frequency domain, the signals were decomposed using 4th Daubechies discrete wavelet transform. To validate the effectiveness of time-frequency approach in characterising vibration fatigue signal, fatigue data was clustered by mapping of the data based on the spectrum energy, root-mean-square and fatigue life obtained. The clustering was performed by comparing the centroid values which both data had five clusters as the optimum data clustering with 0.836 average distance to centroid. From this, the relationship between fatigue life, root-mean-square and spectrum energy can be determined and thus a new fatigue life criterion was developed.

Fatigue Damage Simulation of Automobile Steering Knuckle Subjected to Variable Amplitude Loading

Fatigue damage assessment is essential in determining the durability of automobile components. Several studies have been conducted on fatigue durability of steering knuckle using constant amplitude loading. However, most of the engineering structural and components are subjected to Variable Amplitude Loadings (VALs) in service. Therefore, this study aims to present a fatigue damage simulation of automobile steering knuckle of a 1300 cc national automobile using finite element analysis. In this study, the steering knuckle is modeled using computer-aided design software, in which the dimensions are assigned according to 3D scanning files. The critical area on the steering knuckle is determined using commercial finite element software. The strain gauge is then mounted on the steering knuckle and connected to a data acquisition system to capture the actual fatigue strain signal while driving on a residential road. The fatigue strain signal is then used as the VAL in the fatigue damage simulation of automobile steering knuckle. Forged steel, cast aluminum, and cast iron are used and analyzed in the simulation. Results indicate that the different types of material used significantly influenced the fatigue damage of the automobile steering knuckle.

Evaluation of Fatigue Damage Classification Based on Probabilistic Weibull Analysis

This paper focuses on the evaluation of fatigue damage classification based on statistical probability distribution using Weibull analysis. Feature extractions, i.e., kurtosis, wavelet-based energy, and fatigue damage, were calculated from the segments of fatigue strain signal. The feature extractions were then classified using artificial neural network (ANN) approach in order to find the class or level of fatigue damage. Subsequently, the statistical distribution fitting, i.e., Weibull, was applied to evaluate the fatigue damage classification. Based on the results, the accuracy of the ANN classification was found at 92% and a total of five classes or levels of fatigue damages were obtained. Based on Weibull distribution, when the maximum fatigue damage for the first class is inserted at approximately 8.42 × 10−5, around 69% of the coil spring will have a probability of failure. When the higher fatigue damage is inserted in the fifth class at 1.65 × 10−3, about 99% of the component will have probability to failure. The results show that fatigue damage classification is consistent with the Weibull distribution.

K-means clustering analysis and artificial neural network classification of fatigue strain signals

This paper focuses on the analysis based on the clustering and the classification method of fatigue strain signals. Very few detailed studies have been carried out on the classification of fatigue damage, especially in the automotive field. Fatigue strain signals were observed on the coil springs of vehicles during road tests. The strain signals were then extracted using the Wavelet Transform approach. The features extraction was grouped using the K-means clustering method to obtain the appropriate number of data groups. A classification process was executed to obtain the optimum pattern recognition through the use of artificial neural network (ANN). Based on the results of the ANN classification with an accuracy of 92 %, a total of five classes or levels of fatigue damage were obtained. Based on the results, the data distribution was mostly scattered in the lower class, namely in the first class with the fatigue damage ranging from 1.98 × 10−7 to 8.18 × 10−5. The highest fatigue damage was in the fifth class with values ranging from 1.14 × 10−3 to 1.65 × 10−3. Based on this clustering and classification, the level of fatigue damage could be classified into five stages.

Wavelet-Based Feature Extraction Algorithm for Fatigue Strain Data Associated with the <i>k</i>-Means Clustering Technique

The study presents the development of a wavelet-based segmentation algorithm for fatigue life assessment. Strain data was extracted using the Morlet family. The extraction process identified damaging segments, and it was able to shorten the original signal by 74.3%, with less than 10% difference with statistical parameters. The extraction algorithm was able to retain at least 97.9% of fatigue damage. The damaging segments drawn were clustered using the k-means method to provide three groups of segments, i.e., lower, moderate, and higher groups representing statistical values. The approach was suggested as an alternative method for evaluating and clustering fatigue strain signals.

On the need to decompose fatigue strain signals associated to fatigue life assessment of the AISI 1045 carbon steel

The evaluation of a new statistical-based analysis is discussed in this paper, leading to the fatigue life assessment at three different applied stresses during the cyclic testing procedure. Fatigue tests were performed following the standard ASTM: E466-07. These tests involve a strain gauge being attached to the specimen. For this test, AISI 1045 carbon steel was used as material due to its wide application in automotive and machinery industries. Fatigue tests were performed at three different stress levels of 305MPa, 325MPa, and 345MPa with the testing frequency of 8Hz, and the strain signals were collected accordingly. The Integrated Kurtosis-based Algorithm for Z-filter (I-kaz) approach, which provides its coefficient and three dimensional graphics, was utilised to define a statistically-based fatigue behaviour pattern. The I-kaz technique was used to extract strain signal patterns at the respective low, medium, and high frequency ranges for each signal at specific applied testing stress level. Results showed that highest strain amplitude occur at the low frequency range, suggesting the capability of I-kaz to identify fatigue damage pattern of metallic materials using statistical representation.

Evaluating ultrasound signals of carbon steel fatigue testing using signal analysis approaches

The application of ultrasound techniques to monitor the condition of structures is becoming more prominent because these techniques can detect the early symptoms of defects such as cracks and other defects. The early detection of defects is of vital importance to avoid major failures with catastrophic consequences. An assessment of an ultrasound technique was used to investigate fatigue damage behaviour. Fatigue tests were performed according to the ASTM E466-96 standard with the attachment of an ultrasound sensor to the test specimen. AISI 1045 carbon steel was used due to its wide application in the automotive industry. A fatigue test was performed under constant loading stress at a sampling frequency of 8 Hz. Two sets of data acquisition systems were used to collect the fatigue strain signals and ultrasound signals. All of the signals were edited and analysed using a signal processing approach. Two methods were used to evaluate the signals, the integrated Kurtosis-based algorithm for z-filter technique (I-kaz) and the short-time Fourier transform (STFT). The fatigue damage behaviour was observed from the initial stage until the last stage of the fatigue test. The results of the I-kaz coefficient and the STFT spectrum were used to explain and describe the behaviour of the fatigue damage. I-kaz coefficients were ranged from 60 to 61 for strain signals and ranged from 5 to 76 for ultrasound signals. I-kaz values tend to be high at failure point due to high amplitude of respective signals. STFT spectrogram displays the colour intensity which represents the damage severity of the strain signals. I-kaz technique is found very useful and capable in assessing both stationary and non-stationary signals while STFT technique is suitable only for non-stationary signals by displaying its spectrogram.

Fatigue Damage Assessment Correlating with I-Kaz Coefficient

This paper describes the correlation between fatigue life with the I-kaz coefficients. Fatigue tests were performed according to the ASTM E466-96 standard with a strain gauge attached to the specimen being tested. AISI 1045 carbon steel was used as the material for this test due to its wide applications in the automotive and machinery industry. Fatigue tests were carried out at several constant loading stresses of 610 MPa, 650 MPa and 690 MPa at the sampling frequency of 8 Hz. A set of data acquisition system was used to collect the fatigue strain signals. The integrated Kurtosis-based algorithm for Z-filter (I-kaz) technique had been used to find the I-kaz coefficient. The I-kaz coefficient is found to have a good correlation with fatigue life, other than can represent fatigue damage.

The Morlet and Daubechies Wavelet Transforms for Fatigue Strain Signal Analysis

This paper presents the convenient wavelet family for the fatigue strain signal analysis based on the wavelet coefficients. This study involves the Morlet and Daubechies wavelet coefficients using both the Continuous and Discrete Wavelet Transforms, respectively. The signals were collected from a front lower suspension arm of a passenger car by placing strain gauges at the highest stress locations. The car was driven over public road surfaces, i. e. pavé, highway and UKM roads. In conclusion, the Daubechies wavelet was the convenient wavelet family for the analysis. It was because the wavelet gave the higher wavelet coefficient values indicating that the resemblance between the wavelet and the signals was stronger, closer and more similar.

Fatigue Behaviour Monitoring of an AISI 1045 Carbon Steel using the Statistical-Based Z-Notched Approach

Abstract In this work, three percentages of constant loading are used for fatigue failure assessment. Fatigue behaviour under these three percentages of constant loadings is analyzed by using a new statistical-based approach known as integrated kurtosis-based algorithm for Z-notch filter (I-kaz). Fatigue tests were performed according to ASTM E466-96 standard with a strain gauge attached to the specimen being tested. A fatigue test was conducted under three loadings between 600–700 MPa and at a frequency of 8 Hz. A set of data acquisition systems was used to collect the fatigue strain signals. The I-kaz technique, which provides an I-kaz coefficient and a three-dimensional (3D) graphic, was used to describe and to evaluate the fatigue failure activities. The trends of I-kaz coefficients and of 3D graphics were compared to explain the fatigue damage induced by the three loadings.

Assessment of Fatigue Behaviour under Different Loading Sequences Using Signal Analysis Approaches

Abstract Constant amplitude data obtained from smooth specimens have been used for many years to evaluate the fatigue life of components. However, most of the loadings used in real applications are variable or random. In this research, three types of loading were applied – constant, low–high (LH), and high–low (HL) loading sequences. This paper presents an assessment of the fatigue behaviour under these three types of loading, and the effects of each loading on the fatigue behaviour as well as on life are highlighted. Fatigue tests were performed according to ASTM E466-96 standard with a strain gauge attached to the specimen being tested. AISI 1045 carbon steel was used as material for this tests due to its wide applications in the automotive industry. Fatigue tests were carried out at a constant, LH, and HL loading stresses at a sampling frequency of 8 Hz. A set of data acquisition system was used to collect the fatigue strain signals. All signals obtained were analyzed using two signal-processing approaches – the power spectral density (PSD) method and the integrated Kurtosis-based algorithm for Z-filter (I-kaz) technique. Fatigue behaviour was observed from the initial until the last stage of the fatigue test. The PSD peak values and I-kaz coefficients were determined to explain and describe the behaviour of fatigue damage. Both results are comparable in the fatigue damage assessment.

Influence of spectrum loading sequences on fatigue life in a high-temperature environment

This paper discusses the fatigue life behaviour of aluminium alloy AA6061-T6 under spectrum loadings. Load sequences in spectrum loadings can have significant effects on fatigue life at room temperature and within the elevated temperature range. The main objective of this paper is to investigate the influences of load sequences effect on fatigue life at elevated temperature. Fatigue strain signal was obtained from the engine mount bracket of an automobile under normal driving conditions. Constant amplitude loading, high-to-low, and low-to-high loading sequences were then derived from the original fatigue strain signal to observe the fatigue behaviour at both room and elevated temperatures. The fatigue test was performed on AA6061-T6 specimen according to the ASTM E466 standard using a 100kN servo-hydraulic fatigue testing machine within the temperature range of 27–250°C. The elevated temperature range was chosen based on the maximum temperature of the engine mount bracket and the extreme temperature of the cylinder head that can be reached in service. After the test, fatigue fracture surfaces were sectioned and inspected using a high-magnification microscope. Results show that fatigue life behaviour at room temperature was significantly influenced by the load sequences in spectrum loadings. On the other hand, the effect of load sequences at a higher temperature was reduced.

Fatigue Feature Classification for Automotive Strain Data

Fatigue strain signal were analysed using data segmentation and data clustering. For data segmentation, value of fatigue damage and global statistical signal analysis such as kurtosis was obtained using specific software. Data clustering were carried out using K-Mean clustering approaches. The objective function was calculated in order to determine the best numbers of groups. This method is used to calculate the average distance of each data in the group from its centroid. Finally, the fatigue failure indexes of metallic components were generated from the best number of group that has been acquired. Based on four data collect from two different roads which are D1, D2, the index value generated is not the same for all of data because due to K-Mean clustering, the best group is different for each of the data used. The maximum indexes generated are different for two types of road and namely the index 4 for D1 and index 5 for D2. Due to the road surface condition, higher distributions of the best groups give higher values of index and reflect to higher fatigue damage experienced by the system.

Extracted Fatigue Damaging Events Using the Morlet Wavelet-based Algorithm

This paper presents an algorithm for wavelet coefficient extraction for fatigue strain signal editing based on the Morlet wavelet parameter. In order to achieve the objective of this paper, three mission signals produced at three gate values were simulated for the purpose of the verification of the effectiveness of the fatigue features extraction. The effectiveness was determined by comparing the global signal statistical parameter and total fatigue damaging values between the original and mission shortened signals. Ideally, all signals have approximately the same values. For the related analysis of this paper, the value of 37,000 /Hz was selected as the optimum gate value since the level did not change the signal behaviour. When in use, this gate value gave 101 seconds shortened signal, containing at least 90.8% of the original fatigue damage. In conclusion, this study found that the use of the Morlet wavelet has led to the development of a method to summarise a fatigue strain signal, whilst preserving...

The Morlet wavelet analysis for fatigue feature clustering

This paper presents clustering of fatigue features resulted from the segmentation of SAESUS time series data. The segmentation process was based on the Morlet wavelet coefficient amplitude level which produced 49 segments that each has overall fatigue damage. Observation of the fatigue damage and the wavelet coefficients was made on each segment. At the end of the process, the segments were clustered into three in order to identify any improvements in the data scattering for fatigue data clustering prospects. This algorithm produced a more reliable and suitable method of segment by segment analysis for fatigue strain signal segmentation. According to the findings, the higher Morlet wavelet coefficient presented damaging segment, otherwise, it was non-damaging segment. This indicated that the relationship between the Morlet wavelet coefficient and the fatigue damage was strong and parallel.