A study on the condition based maintenance evaluation system of smart plant device using convolutional neural network

Shin, Mi-Kyeong; Jo, Woo Jin; Min, Hye Jo; Lee, Soo−Hong

doi:10.1007/s12206-020-0526-4

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…There are 5652 words in the text, 3821 words in Google translation, 3805 words in Baidu translation, 3614 words in iFLYTEK translation, and 3777 words in translation. The author compares the definitions of mustard song, listening degree, and iFLYTEK with the translation file of more than 20,000 words word by word [ 20 , 21 ]. Before comparing the whole UI, the author first randomly takes iFLYTEK as the first analysis object to find out the unsuccessful translation of the target language and mark all errors from beginning to end.…”

Section: Experiments and Analysismentioning

confidence: 99%

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

Han¹,

Shaoxiu

2022

Computational Intelligence and Neuroscience

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In order to solve the problems of machine translation efficiency and translation quality, this paper proposes an English translation evaluation system based on the BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of the BP neural network algorithm, taking English online translation as the research object, Google’s translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301, respectively, which is much higher than Google translation. A model of machine translation evaluation based on the neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on the neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation.

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Section: Experiments and Analysismentioning

confidence: 99%

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

Han¹,

Shaoxiu

2022

Computational Intelligence and Neuroscience

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“…According to Zhang et al [14], a CBM policy makes better use of system information, reduces the risk of failures and unnecessary maintenance. Therefore, CBM is a maintenance methodology, which, once implemented, is more cost effective than traditional methodologies, which helps to improve equipment reliability, reduce operating costs, improve safety and reduce the frequency and severity of equipment failures [12,[15][16][17][18][19][20]. In addition to the points enumerated above, with the advancement of technology and the arrival of industry 4.0., companies are becoming more and more sensorized, and it becomes evident that CBM is the maintenance that is most suitable and can achieve the best results [17,[21][22][23][24][25][26].…”

Section: Condition-based Maintenance-cbmmentioning

confidence: 99%

“…Summarizing, CBM can be divided into three phases [9,10,18,34]: (i) Data acquisition, where useful data are collected, usually from sensors;…”

Section: Condition-based Maintenance-cbmmentioning

confidence: 99%

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

et al. 2023

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The maintenance paradigm has evolved over the last few years and companies that want to remain competitive in the market need to provide condition-based maintenance (CBM). The diagnosis and prognosis of the health status of equipment, predictive maintenance (PdM), are fundamental strategies to perform informed maintenance, increasing the company’s profit. This article aims to present a diagnosis and prognosis methodology using a hidden Markov model (HMM) classifier to recognise the equipment status in real time and a deep neural network (DNN), specifically a gated recurrent unit (GRU), to determine this same status in a future of one week. The data collected by the sensors go through several phases, starting by cleaning them. After that, temporal windows are created in order to generate statistical features of the time domain to better understand the equipment’s behaviour. These features go through a normalisation to produce inputs for a feature extraction process, via a principal component analysis (PCA). After the dimensional reduction and obtaining new features with more information, a clustering is performed by the K-means algorithm, in order to group similar data. These clusters enter the HMM classifier as observable states. After training using the Baum–Welch algorithm, the Viterbi algorithm is used to find the best path of hidden states that represent the diagnosis of the equipment, containing three states: state 1—“State of Good Operation”; state 2—“Warning State”; state 3—“Failure State”. Once the equipment diagnosis is complete, the GRU model is used to predict the future, both of the observable states as well as the hidden states coming out from the HMM. Thus, through this network, it is possible to directly obtain the health states 7 days ahead, without the necessity to run the whole methodology from scratch.

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“…Fault diagnosis can analyze the type and degree of faults from signal sources, providing decision support for timely maintenance and replacement of equipment. Therefore, how to extract characteristic parameters representing the operating status of complex and special monitoring signals is of great research significance for accurately evaluating the status of gearboxes 2 .…”

Section: Introductionmentioning

confidence: 99%

A complexity measurement technique for time-series based on improved KW entropy

Zhu,

Chen,

Hou

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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Considering the energy conservation and emission reduction is a key focus of attention. Owning to its special working condition, the quay crane usually undertake an important role. As a significant machinery in hoisting mechanism, the working states for gearbox is playing a significant role in machinery’s reliable operation. For extracting degradation indicators in huge vibration signals of quay crane, this paper proposes an improved KW symbol entropy for fault degradation feature. So as to keep united for the symbols, the technique introduces the root mean square for normal states signal and take the value as the standard and then combining a parameter named symbol coefficient in forming the unified symbols scale. Besides, a parameter named symbols number is proposed to expand the section of symbols set, the information expressing ability in improved then. Based on this, considering information entropy theory, some complexity index for symbol sequence is calculated, and then, two index are calculated as IKSE and IKDE. In order to verify the accuracy of the method proposed in this paper, an example is introduced to analyze the signals in actual working conditions. The results express that the two indexes can accurately represent the degradation trend of the signals, and for the parameter stability, has a certain application value.

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A study on the condition based maintenance evaluation system of smart plant device using convolutional neural network

Cited by 12 publications

References 12 publications

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

A complexity measurement technique for time-series based on improved KW entropy

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