A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost

Zhang, Dahai; Qian, Lu; Mao, Baijin; Huang, Can; Huang, Bin; Si, Yulin

doi:10.1109/access.2018.2818678

Cited by 437 publications

(240 citation statements)

References 31 publications

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“…XGBoost algorithm [41] are widely used in this area, so they are used and compared in our experiments. Our pre-processing steps are as follows: we split the entire flow for every 10 minute and count the flow volume, this results in a total number of 10,271 time points; In order to predict the volume in the next time window, the forecasting makes use of historical data in the past 24 hours (144 points).…”

Section: B Customer Flow Forecastingmentioning

confidence: 99%

Which Channel to Ask My Question?: Personalized Customer Service Request Stream Routing Using Deep Reinforcement Learning

Liu

Long

et al. 2019

IEEE Access

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Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multiple communication channels to answer customers' questions, for example, chatbot and hotline. On one hand, each channel has limited capacity to respond to customers' requests, on the other hand, customers have different preferences over these channels. The current production systems are mainly built based on business rules, which merely considers tradeoffs between resources and customers' satisfaction. To achieve the optimal tradeoff between resources and customers' satisfaction, we propose a new framework based on deep reinforcement learning, which directly takes both resources and user model into account. In addition to the framework, we also propose a new deep-reinforcement-learning based routing method -double dueling deep Q-learning with prioritized experience replay (PER-DoDDQN). We evaluate our proposed framework and method using both synthetic and a real customer service log data from a large financial technology company. We show that our proposed deep-reinforcement-learning based framework is superior to the existing production system. Moreover, we also show our proposed PER-DoDDQN is better than all other deep Q-learning variants in practice, which provides a more optimal routing plan. These observations suggest that our proposed method can seek the trade-off where both channel resources and customers' satisfaction are optimal.INDEX TERMS Deep Reinforcement Learning, Personalized Customer Service, Time-series data processing

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Section: B Customer Flow Forecastingmentioning

confidence: 99%

Which Channel to Ask My Question?: Personalized Customer Service Request Stream Routing Using Deep Reinforcement Learning

Liu

Long

et al. 2019

IEEE Access

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“…Pioneer implementation of the RTE algorithm could be found in the studies of fault detection of composites wind turbine and fatigue prediction for steel but not yet for fiber‐metal laminates. [ 45–47 ]…”

Section: Introductionmentioning

confidence: 99%

Fatigue Life Prediction of GLARE Composites Using Regression Tree Ensemble‐Based Machine Learning Model

Sai

Chai

Srikanth

2020

Advcd Theory and Sims

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With the advancement of machine learning in leading technologies, it is perceived that machine learning is a new and effective alternative for the classic fatigue life prediction. This paper provides a regression tree ensemble‐based machine learning approach to predict the fatigue life of GLARE composites. In the model, mechanical, geometrical properties and fatigue loading stresses are selected as the training parameters (so‐called features), and the GLARE fatigue life is predicted as the output of the model. Experimental data of a total of 98 pieces of GLARE specimens with nine different layups are used for the training and validation of the machine learning model. Results show that the model can provide good fatigue life prediction accuracy and model stability. The most correlated, either positively or negatively, parameters to the fatigue life span are the stress developed in the aluminum layer, the maximum cyclic stress, alternating stress, and mean fatigue stress.

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“…However, most of them rely on the measurement and processing of vibration signals, which require at least one vibration sensor, which demands extra costs for its proper installation and maintenance [74][75][76][77]. In addition, a technician needs knowledge and a good amount of experience to correctly use such sensors [78][79][80][81][82][83][84]. However, the ESA reported to be able to reveal a large number of relations between the machine parameters [85][86][87][88].…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Dineva¹,

Mosavi²,

Gyimesi³

et al. 2019

Preprint

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Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. Performance of various multi-label classification models are compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost

Cited by 437 publications

References 31 publications

Which Channel to Ask My Question?: Personalized Customer Service Request Stream Routing Using Deep Reinforcement Learning

Which Channel to Ask My Question?: Personalized Customer Service Request Stream Routing Using Deep Reinforcement Learning

Fatigue Life Prediction of GLARE Composites Using Regression Tree Ensemble‐Based Machine Learning Model

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

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