Pattern Recognition on Remanufacturing Automotive Component as Support Decision Making Using Mahalanobis-taguchi System

Yazid, Abu; Rijal, J. Khairur; Awaluddin, M. S.; Sari, Emelia

doi:10.1016/j.procir.2014.07.025

Cited by 25 publications

(11 citation statements)

References 12 publications

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“…The use of T method-3 pattern recognition technique to identify features capable of enhancing the pre-processing inspection of the automotive crankshaft has been demonstrated [67] as one of the first inspection approaches. Similarly, Zhang et al [69] proposed a metal magnetic memory (MMM) inspection technique for predicting the residual life of structural cores.…”

Section: Inspection In Remanufacturingmentioning

confidence: 99%

Achieving remanufacturing inspection using deep learning

et al. 2020

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Deep learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, object detection and localisation, natural language processing, prediction and forecasting systems. With significant applicability, deep learning could be used in new and broader areas of applications, including remanufacturing. Remanufacturing is a process of taking used products through disassembly, inspection, cleaning, reconditioning, reassembly and testing to ascertain that their condition meets new products conditions with warranty. This process is complex and requires a good understanding of the respective stages for proper analysis. Inspection is a critical process in remanufacturing, which guarantees the quality of the remanufactured products. It is currently an expensive manual operation in the remanufacturing process that depends on operator expertise, in most cases. This research investigates the application of deep learning algorithms to inspection in remanufacturing, towards automating the inspection process. This paper presents a novel vision-based inspection system based on deep convolution neural network (DCNN) for eight types of defects, namely pitting, rust, cracks and other combination faults. The materials used for this feasibility study were 100 cm × 150 cm mild steel plate material, purchased locally, and captured using a USB webcam of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies used in the design of inspection systems. This research is the first to apply deep learning techniques in remanufacturing inspection. The proposed method offers the potential to eliminate expert judgement in inspection, save cost, increase throughput and improve precision. This preliminary study demonstrates that deep learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of deep learning algorithms to remanufacturing.

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Section: Inspection In Remanufacturingmentioning

confidence: 99%

Achieving remanufacturing inspection using deep learning

et al. 2020

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“…Improved MTS Algorithm. We used MTS for data classification [18][19][20][21][22][23][24][25]. In MTS, Mahalanobis space (MS; reference group) is obtained using standardized variables of healthy or normal data.…”

Section: Reducing Highly Correlatedmentioning

confidence: 99%

Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning

Zhan

Chen

Cheng

et al. 2020

Computational Intelligence and Neuroscience

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Intelligent medical diagnosis has become common in the era of big data, although this technique has been applied to asthma only in limited contexts. Using routine blood biomarkers to identify asthma patients would make clinical diagnosis easier to implement and would enhance research of key asthma variables through data mining techniques. We used routine blood data from healthy individuals to construct a Mahalanobis space (MS). Then, we calculated Mahalanobis distances of the training routine blood data from 355 asthma patients and 1,480 healthy individuals to ensure the efficiency of MS. Orthogonal arrays and signal-to-noise ratios were used to optimize blood biomarker variables. Receiver operating characteristic (ROC) curve was used to determine the threshold value. Ultimately, we validated the system on 182 individuals based on the threshold value. Out of 35 patients with asthma, MTS correctly classified 94.15% of patients. In addition, 97.20% of 147 healthy individuals were correctly classified. The system isolated 7 routine blood biomarkers. Among these biomarkers, platelet distribution width, mean platelet volume, white blood cell count, eosinophil count, and lymphocyte ratio performed well in asthma diagnosis. In brief, MTS shows promise as an accurate method to identify asthma patients based on 7 vital blood biomarker variables and threshold determined by the ROC curve, thus offering the potential to simplify diagnostic complexity and optimize clinical efficiency.

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“…Malalanobis-taguchi system (MTS) is a new method for pattern recognition [1][2][3]. Since quantum particle swarm optimization(QPSO) algorithm has advantages in variable optimization, this paper uses MTS model and QPSO to reduce the feature variables.…”

Section: Introductionmentioning

confidence: 99%

Variable Screening Optimization Algorithm for Mahalanobis-Taguchi System

Peng

Zheng

Liu

2022

J. Phys.: Conf. Ser.

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This paper proposes a Mahalanobis-Taguchi system variable screening optimization method based on binary quantum behavior particle swarm.The main procedures and methods are as follows, Firstly, the Mahalanobis distance value is calculated by the Gram-Schmidt orthogonalization method.We build the multi-objective mixed planning model. The binary quantum behavior particle swarm optimization algorithm is used to solve the optimal combination. A new prediction system based on Mahalanobis-Taguchi metric is established, and the task of accurate discrimination is accomplished.

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Pattern Recognition on Remanufacturing Automotive Component as Support Decision Making Using Mahalanobis-taguchi System

Cited by 25 publications

References 12 publications

Achieving remanufacturing inspection using deep learning

Achieving remanufacturing inspection using deep learning

Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning

Variable Screening Optimization Algorithm for Mahalanobis-Taguchi System

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