Self-tune linear adaptive-genetic algorithm for feature selection

Genetic algorithm (GA) is an established machine learning technique used for heuristic optimisation purposes. However, this natural selection-based technique is prone to premature convergence, especially of the local optimum event. The presence of stagnant performance is due to low population diversity and fixed genetic operator setting. Therefore, an adaptive algorithm, the Self-Tune Linear Adaptive-GA (STLA-GA), is presented in order to avoid suboptimal solutions in feature selection case studies. STLA-GA performs parameter tuning for mutation probability rate, population size, maximum generation number and novel convergence threshold while simultaneously updating the stopping criteria by adopting an exploration-exploitation cycle. The exploration-exploitation cycle embedded in STLA-GA is a function of the latest classifier performance. Compared to standard feature selection practice, the proposed STLA-GA delivers multi-fold benefits, including overcoming local optimum solutions, yielding higher feature subset reduction rates, removing manual parameter tuning, eliminating premature convergence and preventing excessive computational cost, which is due to unstable parameter tuning feedback

An improved turbomachinery conditionmonitoring method using multivariate statistical analysis

Industrial practitioners require a well-structured, proactive and precise conditionmonitoring package in order to optimize turbomachinery operation. Typically, conventional condition monitoring uses built-in software to capture faults or degradation processes based on threshold limits recommended by the Original Equipment Manufacturer (OEM). However, because OEM manual concurrent monitoring involves abundant information parameters, it is dependent on human intervention, insensitive to the development of machinery faults and tends to generate error-prone outcomes. This study proposes a simplified and advanced healthmonitoring method for turbomachinery using a multivariate statistical analysis (MSA) technique. By exploiting mathematical relationships between OEM recommended variables, the significance of input parameter is identified based on weighting factor. With a highly-correlated input subset, the revised condition monitoring method delivershigher sensitivity and a more accurate performance in investigating machine assessment mode

An improved image processing approach for machinery fault diagnosis

Wavelet analysis has been proven to be effective in analysing non-stationary vibration signals. However, the interpretation of the wavelet analysis results, such as a wavelet scalogram, requires high levels of knowledge and experience, which remains a great challenge to practitioners in the field. Recently, the rapid development and advancement of image processing technologies have shed new light on this challenge. In this study, image features such as Harris Stephens(Harris);speeded-up robust features(SURFs);and binary, robust, invariant, scalable keypoints (BRISKs)were obtained from a red, green, and blue (RGB) colour-filtered wavelet scalogram. Each colour filter generates a set of image features from an RGB-filtered wavelet scalogram. Then, the features were utilised as inputs to the fault classifier, namely the support vector machine (SVM),for fault classification. However, there will be a situation where the classification results from the fault classifier, based on the image generated from the different colour filters, are contradictory to each other. No conclusion can thus be made in these situations. This paper employed the Dempster-Shafer (DS) theory to refine the contradicting results and provide an ultimate conclusion to the machine condition. Therefore, the proposed method has improved the fault classification accuracy from 69% to 78%

Wild bitter gourd improves metabolic syndrome: A preliminary dietary supplementation trial

<p>Abstract</p> <p>Background</p> <p>Bitter gourd (<it>Momordica charantia </it>L.) is a common tropical vegetable that has been used in traditional or folk medicine to treat diabetes. Wild bitter gourd (WBG) ameliorated metabolic syndrome (MetS) in animal models. We aimed to preliminarily evaluate the effect of WBG supplementation on MetS in Taiwanese adults.</p> <p>Methods</p> <p>A preliminary open-label uncontrolled supplementation trial was conducted in eligible fulfilled the diagnosis of MetS from May 2008 to April 2009. A total of 42 eligible (21 men and 21 women) with a mean age of 45.7 ± 11.4 years (23 to 63 years) were supplemented with 4.8 gram lyophilized WBG powder in capsules daily for three months and were checked for MetS at enrollment and follow-up monthly. After supplementation was ceased, the participants were continually checked for MetS monthly over an additional three-month period. MetS incidence rate were analyzed using repeated-measures generalized linear mixed models according to the intention-to-treat principle.</p> <p>Results</p> <p>After adjusting for sex and age, the MetS incidence rate (standard error, <it>p </it>value) decreased by 7.1% (3.7%, 0.920), 9.5% (4.3%, 0.451), 19.0% (5.7%, 0.021), 16.7% (5.4%, 0.047), 11.9% (4.7%, 0.229) and 11.9% (4.7%, 0.229) at visit 2, 3, 4, 5, 6, and 7 compared to that at baseline (visit 1), respectively. The decrease in incidence rate was highest at the end of the three-month supplementation period and it was significantly different from that at baseline (<it>p </it>= 0.021). The difference remained significant at end of the 4th month (one month after the cessation of supplementation) (<it>p </it>= 0.047) but the effect diminished at the 5th and 6th months after baseline. The waist circumference also significantly decreased after the supplementation (<it>p </it>< 0.05). The WBG supplementation was generally well-tolerated.</p> <p>Conclusion</p> <p>This is the first report to show that WBG improved MetS in human which provides a firm base for further randomized controlled trials to evaluate the efficacy of WBG supplementation.</p

Feature selection tree for automated machinery fault diagnosis

Intelligent machinery fault diagnosis commonly utilises statistical features of sensor signals as the inputs for its machine learning algorithm. Due to the abundance of statistical features that can be extracted from raw signals and the accuracy of inserting all the available features into the machine learning algorithm for machinery fault classification, less accurate fault classification may inadvertently result due to overfitting issues. It is therefore only by selecting the most representative features that overfitting outcomes can be avoided and classification accuracy be improved. Currently, the genetic algorithm (GA) is regarded as the most commonly used and reliable feature selection tool for the improvement of accuracy for any machine learning algorithm. However, the greatest challenge for GA is that it may fall into a local optima and be computationally demanding. To overcome this limitation, a feature selection tree (FST) is here proposed. Numerous experimental dataset feature selections were executed using FST and GA; their performance is compared and discussed. Analysis showed that the proposed FST resulted in identical or superior optimal feature subsets when compared to the renowned GA method, but with a 20-time faster simulation period. The proposed FST is therefore more efficient in performing feature selection task than GA

A hybrid k-means-GMM machine learning technique for turbomachinery condition monitoring

Industrial practise typically applies pre-set original equipment manufacturers (OEMs) limits to turbomachinery online condition monitoring. However, aforementioned technique which considers sensor readings within range as normal state often get overlooked in the developments of degradation process. Thus, turbomachinery application in dire need of a responsive monitoring analysis in order to avoid machine breakdown before leading to a more disastrous event. A feasible machine learning algorithm consists of k-means and Gaussian Mixture Model (GMM) is proposed to observe the existence of signal trend or anomaly over machine active period. The aim of the unsupervised k-means is to determine the number of clusters, k according to the total trend detected from the processed dataset. Next, the designated k is input into the supervised GMM algorithm to initialize the number of components. Experiment results showed that the k-means-GMM model set up not only capable of statistically define machine state conditions, but also yield a time-dependent clustering image in reflecting degradation severity, as a mean to achieve predictive maintenance

Feature selection tree for automated machinery fault diagnosis

Intelligent machinery fault diagnosis commonly utilises statistical features of sensor signals as the inputs for its machine learning algorithm. Due to the abundance of statistical features that can be extracted from raw signals and the accuracy of inserting all the available features into the machine learning algorithm for machinery fault classification, less accurate fault classification may inadvertently result due to overfitting issues. It is therefore only by selecting the most representative features that overfitting outcomes can be avoided and classification accuracy be improved. Currently, the genetic algorithm (GA) is regarded as the most commonly used and reliable feature selection tool for the improvement of accuracy for any machine learning algorithm. However, the greatest challenge for GA is that it may fall into a local optima and be computationally demanding. To overcome this limitation, a feature selection tree (FST) is here proposed. Numerous experimental dataset feature selections were executed using FST and GA; their performance is compared and discussed. Analysis showed that the proposed FST resulted in identical or superior optimal feature subsets when compared to the renowned GA method, but with a 20-time faster simulation period. The proposed FST is therefore more efficient in performing feature selection task than GA

A hybrid k-means-GMM machine learning technique for turbomachinery condition monitoring

Industrial practise typically applies pre-set original equipment manufacturers (OEMs) limits to turbomachinery online condition monitoring. However, aforementioned technique which considers sensor readings within range as normal state often get overlooked in the developments of degradation process. Thus, turbomachinery application in dire need of a responsive monitoring analysis in order to avoid machine breakdown before leading to a more disastrous event. A feasible machine learning algorithm consists of k-means and Gaussian Mixture Model (GMM) is proposed to observe the existence of signal trend or anomaly over machine active period. The aim of the unsupervised k-means is to determine the number of clusters, k according to the total trend detected from the processed dataset. Next, the designated k is input into the supervised GMM algorithm to initialize the number of components. Experiment results showed that the k-means-GMM model set up not only capable of statistically define machine state conditions, but also yield a time-dependent clustering image in reflecting degradation severity, as a mean to achieve predictive maintenance

Diagnosis of reduction gear failures of a power generation gas turbine due to excessive pipe strain

This paper presents a case study in diagnosing a frequent fatigue failure of a reduction gear used in power generation in oil and gas industry. The reduction gear was connected to a main oil pump (MOP) via a coupling which in turn draw oil from oil reservoir via a series of connecting pipes. An initial investigation conducted by the plant personnel found that the root cause of the frequent gear failure was caused by the recurring misalignments between the reduction gear and the MOP which had resulted in many unscheduled downtimes. Misalignment of the machines recurred times and again in just a short period of time right after the completion of machines alignment works. In view of the complexity of the problem, a vibration Operational Deflection Shape (ODS) analysis of the entire machine train was deployed to obtain a more comprehensive insight into the problem. Results of the ODS analysis revealed that the actual root cause of them is alignment could be traced to pipe strain or misalignment of the pipes that connected to the MOP. Pipe strain was evidenced from the excessive out of phase between the two-connecting pipe flange that routed to MOP. Pipe strain exerts excessive pulling force that caused misalignment to MOP and the entire machines train. The condition of misalignment aggravated as the X2forcing frequency of misalignment coincided with the natural frequency of the MOP itself. MOP resonance had thus triggered a feedback loop that enabled the exchange of vibration energy between resonance and pipe strain. In short, pipe strain had initiated misalignment in MOP and subsequently triggered MOP resonance that fed more vibration energy into the connecting pipe creating a much larger pipe strain. The vicious cycle continues until failures eventually occurred in the reduction gear that connected to the MOP. The remedial works undertaken was thus to eliminate the pipe strain by replacing a section of a rigid pipe with a flexible braided pipe. This enables the system to be more tolerable to pipe strain and also decouples pipe strain from triggering misalignment and resonance in MOP. The remedy measure had successfully resolved the frequent gear failures and also reduced the overall vibration level of the entire machine train by almost half

Review of Underground Storage Tank Condition Monitoring Techniques

This article aims to provide a comprehensive review on the condition monitoring techniques of underground storage tanks (UST). Generally, the UST has long been a favourite toxic substance reservation apparatus, thanks to its large capacity and minimum floor space requirement. Recently, attention has been drawn to the safety risks of the complex cylindrical-shaped system and its surrounding environment due to contamination resulting from unwanted subsurface leakage. Studies on related countermeasures shows that numerous efforts have been focused on the damage remediation process and fault detection practice; however, it has also been observed that there are uncertainties in present technical complications involving the effectiveness of corrective actions and the robustness of condition monitoring techniques. As an alternative means to deliver spatial information on structural integrity, the feasibility of integrating non- destructive evaluation (NDE) techniques with machine learning algorithms, on observing the degradation process of UST, so as to enhance condition monitoring competency, is discussed