Pemantauan dan penilaian retak lesu ke atas talian paip gas dengan menggunakan teknologi pancaran akustik

Fenomena kegagalan lesu dalam talian paip gas sewajarnya dikesan sejak peringkat awal lagi kerana akibatnya yang sangat buruk, dan aplikasi teknologi pancaran akustik dijangka sesuai untuk tujuan mi. Objektif utama projek mi adalah untuk mengkaji korelasi antara isyarat pancaran akustik dengan peringkat-peringkat tertentu dalam mekanisme lesu bagi keluli talian paip API 5L X70 yang jarang ditemui sebelum mi. Bagi mencapai objektif tersebut, ujian makmal telah dilakukan dan dibahagikan kepada dua bahagian iaitu ujian tegangan dan ujian lesu. Ketika ujian lesu, spesimen dikenakan tegasan berkitar balikan sepenuhnya dengan frekuensi bebanan 5Hz dan amplitud tegasan sebanyak 65%, 60%, 58% dan 53% daripada tegasan muktamad. Isyarat pancaran akustik turut dicerap daripada kedua-dua bahagian ujian tersebut. Selain ujian makmal, ujian lapangan turut dilakukan dengan mencerap isyarat pancaran akustik daripada bahagian simpang talian paip gas yang sedang beroperasi untuk tujuan perbandingan. Berdasarkan keputusan ujian tegangan yang dilakukan terhadap keluli API 5L X70, didapati bahawa nilai tegasan alah, tegasan muktamad clan terikan keseluruhan yang dicatatkan masing-masing adalah 637.8 MPa, 706.5 MPa, dan 0.246 mmlmm. Hasil kajian korelasi isyarat pancaran akustik dengan lengkung tegasan-terikan bagi keluli API 5L X70 telah menunjukkan bahawa aktiviti pancaran akustik sangat aktif pada ubah bentuk e1astik. Korelasi isyarat pancaran akustik dengan ujian lesu bagi spesimen yang dikenakan tegasan amplitud yang berlainan telah menunjukkan bahawa trend taburan aktiviti pancaran akustik yang dicerap dapat dibahagikan kepada tiga peringkat. Ketiga-tiga peringkat mi sangat dipengaruhi oleh fenomena pengerasan berkitar dan perlembutan berkitar yang berlaku semasa beban berkitar dikenakan. Analisis ciri-ciri pancaran akustik bagi isyarat-isyarat yang terjana pada ketiga - tiga peringkat di dalam mekanisme lesu menunjukkan bahawa nilai frekuensi dominan bagi isyarat-isyarat domain frekuensi daripada mekanisme lesu dengan operasi sebenar talian paip telah menunjukkan perbezaan. Nilai frekuensi dominan yang dicatatkan pada peringkat kedua dan ketiga dalam mekanisme lesu masing-masing berada pada julat antara 314 kHz hingga 364 kHz, dan 312 kHz hingga 380 kHz. Manakala nilai frekuensi dominan bagi isyarat domain frekuensi daripada ujian lapangan berjulat antara 50.05kHz hingga 68.36 kHz. Namun, perbandingan nilai frekuensi dominan bagi isyarat-isyarat yang dicerap pada ketiga-tiga peringkat semasa mekanisme lesu tidak menunjukkan perbezaan yang jelas. Keputusan analisis parameter statistik bagi isyarat-isyarat domain masa pancaran akustik daripada ujian lesu dan ujian lapangan menunjukkan bahawa nilai kurtosis hanya dapat memberikan perbezaan antara isyarat yang dicerap daripada ujian lapangan dan peringkat retak sahaja. Oleh itu analisis komponen utama telah diaplikasikan dan analisis mi ditemui berupaya untuk membezakan isyarat-isyarat domain frekuensi daripada ketiga-tiga peringkat semasa mekanisme lesu dan operasi sebenar talian paip gas. Modifikasi persembahan keputusan analisis komponen utama dengan mengaplikasikan plot dendrogram telah menunjukkan perbezaan kelompok isyarat pancaran akusfik daripada peringkat yang berbeza dengan lebih jelas. Isyarat pancaran akustik yang dicerap daripada lapangan turut berada pada kelompok yang sangat berbeza daripada kelompok isyarat-isyarat daripada mekanisme lesu. Secara keseluruhannya, dapat dirumuskan bahawa korelasi isyarat pancaran akustik dengan mekanisme lesu dapat dijadikan panduan di dalam memantau dan menilai fenomena lesu dalam talian paip semasa operasi

Monitoring of Engine Lubrication using Acoustic Emission Technology

This research presents the application of Acoustic Emission (AE) sensor to explore the feasibility of an AE signal in the process of monitoring the engine oil viscosity on different condition of mileage during the engine operation. The viscosity experiment was performed in a prior stage of the experiment using four samples of SAE 5W-30 engine oil with the different condition of mileage. On the next occasion, the AE signal was acquired on three cylinders of a four-stroke gasoline engine with a cooling water system which operates using the previous lubricant sample. In this experiment, the AE sensor was bonded on the outside of the engine block and located at the oil filter and oil sump location. It was found that the AE energy value is capable of determining the variant of the engine oil viscosity. The results from this project show that via AE technology proved to be feasible for the development of a real-time lubricant condition monitoring which was believed to be a new technology in the automotive industry

Monitoring and assessment of weld penetration condition during pulse mode laser welding using air-borne acoustic signal

Real-time monitoring system is one of the essential criteria in the era of the fourth industrial revolution (Industry 4.0). Among the monitoring systems in laser welding applications, acoustic methods have recently caught the attention of researchers due to their benefits in promoting simple, low-cost, and non-contact systems. However, applying this method in PW mode laser was challenging due to the different characteristic of signal and noise acquired from this process as compared to CW process. Therefore, this particular work aims to investigate the characteristics of acoustic sound signal from PW Fiber laser, develop an appropriate signal processing algorithm to suppress the effect of noise on the extracted sound features, and develop an empirical model for weld depth estimation. To achieve the objectives, a 1.8 mm thick 22MnB5 boron steel plate was welded with varied laser peak power (PP) and pulse duration (PD) levels. Simultaneously, the sound signal was acquired between the frequency of 20 Hz to 12.8 kHz throughout the process. Signal features, such as mean absolute deviation (MAD), standard deviation (SD), kurtosis (K), L-scale (LS), L-kurtosis (LK), bandpower (BP), and sum of synchrosqueezed wavelet coefficient (CSqWCsum) were extracted from the acquired sound. To develop the signal processing algorithm, multi-lag phase space (MLPS) method was adopted in which some modifications on its original algorithm were made by introducing the localized crest factor (CF) thresholding method to reduce the influence of noise. Results showed that the acquired sound recorded transient behaviors with a slight change in its overall amplitudes with respect to the change in the level of weld parameters. Meanwhile, the dominant frequency was found to be fluctuated between 5760 Hz and 7000 Hz without a clear pattern in the case of different levels of weld parameters involved in this study. The results from feature selection analysis show that the combination of SD, L-kurtosis, and modified-MLPS recorded the most significant relation with weld penetration. Furthermore, the combination of these features with the laser peak power and pulse duration recorded a better regression trend with an adjusted R-squared of 0.937. Two empirical models for weld depth estimation were developed from the combination of these sound features and weld parameters using the multiple linear regression (MLR) and artificial neural network (ANN) methods. Through MLR method, the obtained model was DOP = 0.634SD - 0.814LK + 0.0014MLPS + 116.44PD + 0.0014PP - 0.7781. Results from the model validation analysis showed that both models could significantly estimate weld penetration during the PW laser welding process with an estimation error less than 8%. However, the ANN model recorded a more accurate and precise estimation with the lowest estimation error, i.e., 3.3%. The results of the analysis suggest that the acoustic methods can be used to monitor weld penetration on a real-time basis during PW mode laser welding process. Moreover, the methods can also be used to provide a quantitative assessment on weld penetration during the process. This finding gives alternative solution to the development of a real-time process monitoring system in PW mode laser welding, which aligns with the criteria needed in the new era of manufacturing system

Detection of irregularities on weld bead from the L-Statistic analysis of the acquired sound during pulse mode laser welding process

Since several past decades, many studies prove that the statistical or signal features extracted from the sound acquired from laser welding process was significantly giving information on the weld condition. However, a considerable amount of studies were only emphasizing on the use of common statistical features in which it is restricted to some limitation when dealing with non-stationary random sound signal. In this particular work, the main aim was set to detect the irregularities along the weld bead by way of implementing the L-Statistic analysis on the acquired sound during pulse mode laser welding process. To achieve the goal, pulse mode laser welding have been done onto 22MnB5 boron steel plate in butt joined configuration. During the process, sound signal was acquired using microphone and further analyzed by extracting L-statistic features from it. According to the findings, among all the L-statistic features analyze in this study, L-Cv (scale) was found giving a significant indicator of the weld bead surface condition. Larger value of L-Cv was recorded at the point where the large underfill occurred. On the other hand, it was also found that the L-kurtosis values could give remarkable information on the existence of the irregularities on bead width and depth. Hence, it could be drawn into conclusion that the irregularities on the weld bead during the pulse mode laser welding could be detected from the appropriate L-statistic features of the acquired sound signal. The finding in this work was believed to be essential in enhancing the capability of acoustic sound method to be developed as online monitoring system for pulse mode laser welding process

Detection Of Defects On Weld Bead Through The Wavelet Analysis Of The Acquired Arc Sound Signal

Recently, the development of online quality monitoring system based on the arc sound signal has become one of the main interests due its ability to provide the non-contact measurement. Notwithstanding, numerous unrelated-to-defect sources which influence the sound generation are one of the aspects that increase the difficulties of applying this method to detect the defect during welding process. This work aims to reveal the hidden information that associates with the existence of irregularities and porosity on the weld bead from the acquired arc sound by applying the discrete wavelet transform. To achieve the aim, the arc sound signal was captured during the metal inert gas (MIG) welding process of three API 5L X70 steel specimens. Prior to the signal acquisition process, the frequency range was set from 20 Hz to 10 000 Hz which is in audible range. In the next stage, a discrete wavelet transform was applied to the acquired sound in order to reveal the hidden information associated with the occurrence of discontinuity and porosity. According to the results, it was clear that the acquired arc sound was not giving an obvious indication of the presence of defect as well as its location due to the high noise level. More interesting findings have been obtained when the discrete wavelet transform (DWT) analysis was applied. The analysis results indicate that the level 8 of the approximate and detail wavelet coefficient have given a significant sign associated with the presence of irregularities and porosity respectively. Moreover, despite giving the information on the surfaces pores, the detail wavelet coefficient was found to give a clear indication of the sub-surface porosity formation during welding process. Hence, it could be concluded that the hidden information with respect to the occurrence of discontinuity and porosity on the weld bead could be obtained by applying the discrete wavelet transfor

Acoustic methods in real-time welding process monitoring: Application and future potential advancement

The rapid advancement of the welding technology has simultaneously increased the demand for the online monitoring system in order to control the process. Among the methods that could be possibly used to assess the weld condition, an air-borne acoustic method grasps the attention from scholars due to its ability to provide a simple, non-contact, and low-cost measurement system. However, it is still lack of resources involving this subject in an attempt to deeply understand the emitted sound behaviour during welding especially when dealing with a complete deviation of a process parameter, welding types, workpiece material as well as the noise from the surrounding. This paper reviews the application of the acoustic method in monitoring the welding process. Specifically, this review emphasized the source of both structure-borne and air-borne acoustic during the welding process and the significance of applying the acoustic method in more detail. By focusing on the liquid state welding process, the scope of discussion converged on the arc and laser welding process. In the last part of this review, the potential future advancement of this method is pointed out before the overall conclusion is made

Defect identification during pulse mode laser welding process through the pattern recognition analysis of the acquired sound frequency spectrum

Problems on laser weld quality still remain as vital issue even though the process was done with optimized condition which results the demand on robust monitoring method during the process. Until recently, many methods have been explored and air-borne acoustic are among of methods that have been proven to be able to detect the presence of defect. However, despite detection, it is essential if the type of defect could be identified as it gives different severity level to the development of failure. This work presents the identification of defect during pulse mode laser welding through the analysis of sound. In achieving the goal of this study, bead on plate weld have been done onto the 22MnB5 boron steel plate repeatedly based on 3 different set of experiment with the variation in the level of parameters. Simultaneously, time-series sound signal was acquired along the process before it was converted into frequency spectrum before further analysis. According to the result, it was recorded that the variation of parameters level in pulse mode laser welding process lead to the presence of porosity and crack. Relatively, the trend of sound frequency spectrum were also significantly changes its trend in respond to the parameters level variation. It was discovered that the dominant frequency for the signals acquired from the process which produce good quality weld, porosity and crack recorded the same range which was between 5 to 7 kHz. Uniquely, the existence of porosity could be identified by the occurrence of peak at around 9 kHz while the presence of crack could be recognized by the occurrence of peak at 8 kHz and 11 kHz. This trend was proven to be consistent in repeated experiment according to the result from principal component analysis. Based from the result in this study, it could be conclude that the identification of defect could be done by the analysis of the acquired sound during the process. Significantly, this would expand the ability of acoustic method for quality monitoring purpose as the identification of defect is also important in quality control

Mel-frequency cepstral and spectral flux analysis of the acoustic signal for real-time status monitoring of laser cleaning

Due to the fact that the laser-based cleaning process is quick, efficient, and environmentally friendly, it has been utilized in a various industry, which has increased the number of studies pertaining to this process. In addition to process optimization, the real-time monitoring system was essential in preventing the overexposure of the laser beam to the cleansed surface, which would result in an engraving effect. This article demonstrated the analysis of the acquired sound signal to identify an overexposed laser beam during laser cleaning. In order to accomplish the aim of this work, the corroded boron steel plate was prepared. The laser cleaning procedure involved a four-loop laser scan. Variable scanning speeds between 100 and 1,000 mm/s were configured. Concurrently, the acoustic signal within the frequency range of 20 Hz to 10 kHz was acquired. The results indicate that the process with a scanning speed of 1000 mm/s recorded the clear surface without morphological change on the cleaned area, whereas an unacceptable deep gouge was formed during the second and third loops of the process with speeds of 100 mm/s and 300 mm/s, respectively. According to an analysis of the acquired sound signal, the trend of the Mel Frequency Cepstral Coefficient (MFCC) was indicative of the existence of the ablated corroded substrate. In addition, the spectral flux can provide important information regarding the formation of a deep groove on a cleansed surface. This research demonstrates the feasibility of using the auditory signal to monitor the laser cleaning process. By characterizing the acoustic signal feature, it is possible to detect the completion of the cleaning process before the morphological change of the cleaned area existed. With further development, it was possible that this method would become the most efficient, resilient, and demanding in the future

A wavelet decomposition analysis of vibration signal for bearing fault detection

This paper presents a study of vibrational signal analysis for bearing fault detection using Discrete Wavelet Transform (DWT). In this study, the vibration data was acquired from three different types of bearing defect i.e. corroded, outer race defect and point defect. The experiments were carried out at three different speeds which are 10%, 50% and 90% of the maximum motor speed. The time domain vibration data measured from accelerometer was then transformed into frequency domain using a frequency analyzer in order to study the frequency characteristics of the signal. The DWT was utilized to decomposed signal at different frequency scale. Then, root mean square (RMS) for every decomposition level was calculated to detect the defect features in vibration signals by referring to the trend of vibrational energy retention at every decomposition. Based on the result, the defective bearings show significant deviation in retaining RMS value after a few levels of decomposition. The findings indicate that Wavelet decomposition analysis can be used to develop an effective bearing condition monitoring tool. This signal processing analysis is recommended in on-line monitoring while the machine is on operation

Application of Ikaz and direct quadrature for solving leakage in pipeline distribution by using transmission line modelling

A new transient-based advance towards single leak detection is proposed which requires a measurement station with an end at the pipe system. The method use the frequency response and gives adequate results using low frequency bandwidth. This research apply Empirical Mode Decomposition (EMD) as the method denoising the noisy pressure transient signal before the signal further analyze using instantaneous frequency analysis. Therefore EMD is the way to decompose into Intrinsic Mode Function (IMF) from the signal. However it is difficult to select suitable IMF. Thus the paper proposed the implementation of Integrated Kurtosis-based algorithm Z-filter technique for that allows automatic selection of relevant and appropriates IMF. This work demonstrated the synthetic pressure transient signal generates using transmission line modelling (TLM) in order to test the effectiveness of Ikaz as the autonomous selection of IMF. This paper implement the Direct Quadrature as the instantaneous frequency analysis. A straight fluid network was designed using TLM fixing with higher resistance at some point act as a leak and connecting to the pipe feature (junction, pipefitting or blockage). The analysis results using Ikaz revealed that the method can be utilized as an automatic selection of intrinsic mode function (IMF) although the noise level ratio of the signal is lower. Ikaz-kurtosis ratio is recommended and advised to be implemented as automatic selection of intrinsic mode function (IMF) through DQ analysis