Papers by Keyword: Mahalanobis Distance

Abstract: This study proposes a Mahalanobis Distance Measurement (MDM) method to analyze current waveform for determining the motor’s quality types. The MDM method consists of three major stages: (i) the preprocessing stage which is for enlarging motor current waveforms’ amplitude and eliminating noises, and includes signal amplitude amplifier, filter circuit (eliminating noises), and analog-to-digital converter (ADC) parts, (ii) the qualitative features stage which is for qualitative feature selection on motor current waveforms, and (iii) the classification stage which is for determining motor quality types using the MDM method. It can recognize defective motors and their defective types in less than 0.5 second. In the experiment, the total classification accuracy (TCA) was approximately 99.03% in average. The proposed method has the advantages of good detection results, no complex mathematic computations, hi-speed, and hi-reliability.

Abstract: The performance assessment of hydraulic servo systems has attracted an increasing amount of attention in recent years. However, only a few studies have focused on practical approaches in this field. A performance assessment method based on radial basis function (RBF) neural network and Mahalanobis distance (MD) is proposed in this study; the method is quantized by the performance confidence value (CV). An observer model based on RBF neural network is designed to calculate the residual error between the actual and estimated outputs. The root mean square (RMS), peak value, and average absolute value are then extracted as the features of residual error, which serve as the coordinates of the feature points. Lastly, the MD between the most recent feature point and the constructed Mahalanobis space is calculated. The condition of the system is assessed by normalizing MD into a CV. The proposed method is proven to be effective by a simulation model in which leakage faults are injected. Experimental results show that the proposed method can assess the performance of hydraulic servo systems effectively.

Abstract: GrowCut algorithm is not only an interactive algorithm on the basis of cell automata, but also a multi-label algorithm based on seeds point. Aiming at the GrowCut algorithm usually asks users to partition foreground and background manually and mark a lot more initial seeds. This paper presents an automatic object segmentation method which combining secondary watershed and GrowCut algorithm, here in the following paper refers it to as SWGC algorithm. It firstly using the twice used watershed algorithm to partition the input image, the segmented regions are labeled using Mahalanobis distance, and merged according to the image color and space information, thereafter applying the GrowCut algorithm to perform globally optimized segmentation. The main contribution focuses on performing automatic segmentation which consist of obtain the foreground and background region and generate the seed template of GrowCut algorithm automatically. Thus not only leave out the constraints of user interaction operation, but also avoid the subjectivity and uncertainty. The proposed method reduces the runtime significantly as well as improves the segmentation accuracy and robustness of GrowCut algorithm. Experimental results show SWGC algorithm has superior performance compared to the other related methods.

Abstract: With the development of computational technology, neural network has attracted the more and more attentions to reveal the relationships between the process parameters and welding geometry. However, the Gas Metal Arc (GMA) welding is complex and of multiple interactions so that mathematical model for welding parameters has not been achieved. Neural networks have been noted as being particularly advantageous for modeling systems which contain noisy, fuzzy and uncertain elements, while a sufficient algorithm is employed. In this study, Levenberg-Marquardt algorithm was employed into GMA welding process. Mahalanobis Distance (MD) was measured to determine the on-line welding quality to avoid joint failure as welding quality. To get an optimal neural network, cases with different configurations were carried out. The Root of the Mean sum of Squared (RMS) error was adopted to evaluate the accuracy of the prediction by neural networks with LM algorithm. The results presented that the proposed algorithm had the superiority of high accuracy that can be used in the on-line welding process.

Abstract: Gas Metal Arc (GMA) welding is considered as a multi-parameter process that it’s hard to find optimal parameters for good welding. To overcome the problem, an artificial neural network based on the backpropagation algorithm was built to realize the relationships between process parameters and welding quality as output parameter. In this study, Mahalanobis Distance (MD) was employed to evaluate the availability of a given welding parameters which was proved to performance well in multivariate statistics. Input parameters such as welding current and arc voltage were chosen due to their significant influence on the welding quality. To improve the precision of given parameters’ evaluation, neural networks with different configurations were verified. The analyses on the measured and predicted MD by the proposed neural network were conducted. The proposed neural network based on the error backpropogation algorithm was proved to have high reliability to evaluate process parameters, which further makes it available in on-line monitoring system.

Abstract: This paper proposes a new approach to the multi-robot map fusion algorithm that enables a team of robots to build a joint map without initial knowledge of their relative pose. First, the relative distance and bearing measurements between two robots are fused together by the covariance intersection method after they detect each other. Second, the transformation equations among multi robots coordinates are derived based on their relative distance and bearing measurements. Third, all the multi robots local maps are merged into one global map by unscented transform based on the transformation equations. Fourth, the possible duplicate features are filtered out by the robots maximal detection area and the features coordinate range, then the Mahalanobis distance is computed to decide the duplicate features correspondence through unscented transform, and the Kalman Filter is used while fusing the duplicate features information. As a means of validation for the proposed method, experimental results obtained from the two robots are presented.

Abstract: To categorize the different patterns of connecting rod based on the extent to which the product is remanufacturable is very challenging because of the existence of various models and wide tolerances. Sometimes it cannot be done due to the improper pattern recognition system. Mahalanobis-Taguchi System (MTS) is a diagnostic method employing Mahalanobis Distance (MD) for recognizing different patterns in multivariate data. The aim of this work is to apply T method-3, a sub-method of MTS, to the big-end diameter of connecting rod to distinguish between two distinct ranges within the remanufacturability process spectrum. Furthermore, the method also categorizes various patterns of connecting rod based on their MD from unit space with graphical illustration. The case study is performed in an automotive industry as well as in a contract remanufacturing environment in Malaysia. The outcome of this work is expected to be the enhancement of robustness in the remanufacturing system on pattern recognition applicable to the company under study. It is expected that the company will experience time and energy savings and improved work quality. The resulting systematic analysis is expected to enable fast decision-making. Finally, this study is expected to invoke among researchers a sense of seriousness in their approach towards various case studies involving the upgrading of the remanufacturing process that will bring Malaysias remanufacturing capability on par with that of other developed countries.

Abstract: While the concept of remanufacturing, especially on automotive parts is gaining in popularity, in practice the remanufacturing industry in Malaysia is still in its nascent stage, with approximately 32 fields in various industries claiming to be involved in the process. The Mahalanobis-Taguchi System (MTS) is a diagnostic method employing Mahalanobis Distance (MD) for recognizing different patterns in multivariate data. The aim of this work is to apply T method-3, which is one of the sub-methods under the MTS relating to the main journal diameter of the crankshaft. The method distinguishes between two distinct ranges of acceptable remanufacturing and non-remanufacturing processes. Furthermore, the method also categorizes various patterns of crankshaft based on their MD in unit space. The case study is performed in an automotive industry in Malaysia under a contract remanufacturing environment. The outcome of this work is expected to be the enhancement of the robustness of the remanufacturing system on pattern recognition to the company under study. As a result, the company is expected to save more time and energy in coming with faster decision-making. In addition, the study would provide greater inspiration, especially among researchers in aggressively applying MTS applications to a wider variety of industry sectors especially in the remanufacturing area.

Abstract: In robotic GMA (Gas Metal Arc) welding process, heat and mass inputs are coupled and transferred by the weld arc and molten base material to the weld pool. The amount and distribution of the input energy are basically controlled by the obvious and careful choices of welding process parameters in order to accomplish the optimal bead geometry and the desired mechanical properties of the quality weldment. To make effective use of automated and robotic GMA welding, it is imperative to predict online faults for bead geometry and welding quality with respect to welding parameters, applicable to all welding positions and covering a wide range of material thickness. To successfully accomplish this objective, two sets of experiment were performed with different welding parameters; the welded samples from SM 490A steel flats adopting the bead-on-plate technique were employed in the experiment. The experimental results of current and voltage waveforms were used to predict the magnitude of bead geometry and welding quality, and to establish the relationships between weld process parameters and online welding faults. MD (Mahalanobis Distance) technique is employed for investigating and modeling of GMA welding process and significance test techniques were applied for the interpretation of the experimental data. Statistical models developed from experimental results which can be used to control the welding process parameters in order to achieve the desired bead geometry based on weld quality criteria.

Abstract: Semi-supervised clustering is a method which can improve clustering performance by introducing partial supervised information. This paper mainly studies the semi-supervised fuzzy clustering which introduces Mahalanobis distance and Gaussian Kernel. And we obtain a new semi-supervised fuzzy clustering objective function. By solving the optimization problem, we propose a semi-supervised fuzzy clustering algorithm F-SCAPC which includes F(M)-SCAPC and F(K)-SCAPC. And we do experimental research for proposed algorithm F-SCAPC using the selected standard data set and the artificial data set. Besides, we compare performance of presented algorithm F-SCAPC with AFFC, KFCM-F and SCAPC algorithms. From the results, we can see that F-SCAPC is effective in the convergence speed and the accuracy.