Browsing by Author "Shivam, Kumar"

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Fault diagnosis method for worm gearbox using convolutional network and ensemble learning
(2020-01-01)
Hsiao, J. C.
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Shivam, Kumar
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Kam, T. Y.
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機械工程學系
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Department of Mechanical Engineering
Worm gearboxes are popular across various industrial applications since they offer significant gear ratios in small installation spaces. Despite having multiple advantages, worm gearboxes are subjected to higher friction due to sliding design and are prone to damage and increased transmission error over the period of operation. Delayed diagnosis of worm gearbox degradation can lead to low-quality products and/or unnecessary production line downtime. Using vibration characteristics of worm gearbox, it is possible to determine the fault and transmission error at a given period in time. In this paper, an ensemble machine learning model is trained and deployed to monitor the transmission error of worm gearbox and classify between new, operational and old conditions. 1D CNN (one dimensional convolutional neural network) model is used to automatically extract features in vibration signal of X, Y, and Z axes and predict the relevant state of worm gear. The proposed technique uses ensemble machine learning technique fusion of features extracted by multi-layer 1D CNN for three axes vibration data. The proposed method could achieve 96% accuracy and performs significantly better than traditional sequential and ensemble machine learning models on a dataset of with 7,870 samples with 800 samples labeled as new condition, 4,740 samples as operational and 2,330 as old.
Shape Design Optimization of a Robot Arm Using a Surrogate-Based Evolutionary Approach
(2020-04-01)
Hsiao, J. C.
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Shivam, Kumar
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Chou, C. L.
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Kam, T. Y.
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機械工程學系
In the design optimization of robot arms, the use of simulation technologies for modeling and optimizing the objective functions is still challenging. The difficulty is not only associated with the large computational cost of high-fidelity structural simulations but also linked to the reasonable compromise between the multiple conflicting objectives of robot arms. In this paper we propose a surrogate-based evolutionary optimization (SBEO) method via a global optimization approach, which incorporates the response surface method (RSM) and multi-objective evolutionary algorithm by decomposition (the differential evolution (DE ) variant) (MOEA/D-DE) to tackle the shape design optimization problem of robot arms for achieving high speed performance. The computer-aided engineering (CAE) tools such as CAE solvers, computer-aided design (CAD) Inventor, and finite element method (FEM) ANSYS are first used to produce the design and assess the performance of the robot arm. The surrogate model constructed on the basis of Box-Behnken design is then used in the MOEA/D-DE, which includes the process of selection, recombination, and mutation, to optimize the robot arm. The performance of the optimized robot arm is compared with the baseline one to validate the correctness and effectiveness of the proposed method. The results obtained for the adopted example show that the proposed method can not only significantly improve the robot arm performance and save computational cost but may also be deployed to solve other complex design optimization problems.