Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

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Comparative Analysis of the Life-Cycle Cost of Robot Substitution: A Case of Automobile Welding Production in China

Symmetry ◽

10.3390/sym13020226 ◽

2021 ◽

Vol 13 (2) ◽

pp. 226

Author(s):

Xuyang Zhao ◽

Cisheng Wu ◽

Duanyong Liu

Keyword(s):

Life Cycle ◽

Systems Engineering ◽

Life Cycle Cost ◽

Manufacturing Systems ◽

Large Scale ◽

Cost Allocation ◽

Industrial Robot ◽

Case Analysis ◽

Industrial Robots ◽

Analysis Model

Within the context of the large-scale application of industrial robots, methods of analyzing the life-cycle cost (LCC) of industrial robot production have shown considerable developments, but there remains a lack of methods that allow for the examination of robot substitution. Taking inspiration from the symmetry philosophy in manufacturing systems engineering, this article further establishes a comparative LCC analysis model to compare the LCC of the industrial robot production with traditional production at the same time. This model introduces intangible costs (covering idle loss, efficiency loss and defect loss) to supplement the actual costs and comprehensively uses various methods for cost allocation and variable estimation to conduct total cost and the cost efficiency analysis, together with hierarchical decomposition and dynamic comparison. To demonstrate the model, an investigation of a Chinese automobile manufacturer is provided to compare the LCC of welding robot production with that of manual welding production; methods of case analysis and simulation are combined, and a thorough comparison is done with related existing works to show the validity of this framework. In accordance with this study, a simple template is developed to support the decision-making analysis of the application and cost management of industrial robots. In addition, the case analysis and simulations can provide references for enterprises in emerging markets in relation to robot substitution.

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A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽

10.3390/s21082599 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2599

Author(s):

Zhenbao Li ◽

Wanlu Jiang ◽

Sheng Zhang ◽

Yu Sun ◽

Shuqing Zhang

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Recognition Accuracy ◽

Ensemble Empirical Mode Decomposition ◽

Hydraulic Pump ◽

Vibration Signals ◽

Mode Decomposition ◽

Diagnosis Method ◽

Kernel Extreme Learning Machine ◽

Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

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Fault diagnosis and prognosis of steer-by-wire system based on finite state machine and extreme learning machine

Neural Computing and Applications ◽

10.1007/s00521-021-06028-0 ◽

2021 ◽

Author(s):

Dun Lan ◽

Ming Yu ◽

Yunzhi Huang ◽

Zhaowu Ping ◽

Jie Zhang

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Finite State Machine ◽

State Machine ◽

Diagnosis And Prognosis ◽

Steer By Wire ◽

Finite State ◽

Learning Machine ◽

Fault Diagnosis And Prognosis ◽

Wire System

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Intelligent Fault Diagnosis of Rolling Bearings Based on Refined Composite Multi-Scale Dispersion q-Complexity and Adaptive Whale Algorithm-Extreme Learning Machine

Measurement ◽

10.1016/j.measurement.2021.108977 ◽

2021 ◽

pp. 108977

Author(s):

Wei Dong ◽

Shuqing Zhang ◽

Anqi Jiang ◽

Wanlu Jiang ◽

Liguo Zhang ◽

...

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Intelligent Fault Diagnosis ◽

Rolling Bearings ◽

Multi Scale ◽

Learning Machine

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Two-step fault diagnosis framework for rolling element bearings with imbalanced data based on GSA-WELM and GSA-ELM

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406217728091 ◽

2017 ◽

Vol 232 (16) ◽

pp. 2937-2947 ◽

Cited By ~ 2

Author(s):

Yuan Lan ◽

Xiaohong Han ◽

Weiwei Zong ◽

Xiaojian Ding ◽

Xiaoyan Xiong ◽

...

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Gravitational Search Algorithm ◽

Imbalanced Data ◽

Rolling Element Bearing ◽

Rolling Element Bearings ◽

Misclassification Cost ◽

Weighted Extreme Learning Machine ◽

Rolling Element ◽

Learning Machine

Rolling element bearings constitute the key parts on rotating machinery, and their fault diagnosis is of great importance. In many real bearing fault diagnosis applications, the number of fault data is much less than the number of normal data, i.e. the data are imbalanced. Many traditional diagnosis methods will get low accuracy because they have a natural tendency to favor the majority class by assuming balanced class distribution or equal misclassification cost. To deal with imbalanced data, in this article, a novel two-step fault diagnosis framework is proposed to diagnose the status of rolling element bearings. Our proposed framework consists of two steps for fault diagnosis, where Step 1 makes use of weighted extreme learning machine in an effort to classify the normal or abnormal categories, and Step 2 further diagnoses the underlying anomaly in detail by using preliminary extreme learning machine. In addition, gravitational search algorithm is applied to further extract the significant features and determine the optimal parameters of the weighted extreme learning machine and extreme learning machine classifiers. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our Institute, and the empirical results show that our approach is really fast and can achieve the diagnosis accuracies more than 96%.

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Short Time Solar Power Forecasting Using Persistence Extreme Learning Machine Approach

E3S Web of Conferences ◽

10.1051/e3sconf/202129401002 ◽

2021 ◽

Vol 294 ◽

pp. 01002

Author(s):

Xiaoyan Xiang ◽

Yao Sun ◽

Xiaofei Deng

Keyword(s):

Solar Energy ◽

Extreme Learning Machine ◽

Power Output ◽

Smart Grids ◽

Large Scale ◽

Solar Power ◽

Learning Algorithm ◽

Power Performance ◽

Energy Prediction ◽

Learning Machine

Solar energy in nature is irregular, so photovoltaic (PV) power performance is intermittent, and highly dependent on solar radiation, temperature and other meteorological parameters. Accurately predicting solar power to ensure the economic operation of micro-grids (MG) and smart grids is an important challenge to improve the large-scale application of PV to traditional power systems. In this paper, a hybrid machine learning algorithm is proposed to predict solar power accurately, and Persistence Extreme Learning Machine(P-ELM) algorithm is used to train the system. The input parameters are the temperature, sunshine and solar power output at the time of i, and the output parameters are the temperature, sunshine and solar power output at the time i+1. The proposed method can realize the prediction of solar power output 20 minutes in advance. Mean absolute error (MAE) and root-mean-square error (RMSE) are used to characterize the performance of P-ELM algorithm, and compared with ELM algorithm. The results show that the accuracy of P-ELM algorithm is better in short-term prediction, and P-ELM algorithm is very suitable for real-time solar energy prediction accuracy and reliability.

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