A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

Wang, Zhongbin; Xu, Xinni; Si, Lina; Ji, Rui; Liu, Xinhua; Tan, Chao

doi:10.1155/2016/9674942

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…Wang et al 63 developed a multi-class identifier based on the self/nonself discrimination mechanism of the immune system to identify normal and dangerous operating modes for mining equipment based on input from real-time operational data. The input data consist of indicators of the health status, which have been selected based on importance through expert judgement.…”

Section: Life Cycle Risk Assessment For Marine Systemsmentioning

confidence: 99%

Developing next generation marine risk analysis for ships: Bio-inspiration for building immunity

Ventikos

Louzis

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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Research on autonomous ships has led to several ideas about how they could be operated in terms of level of automation and human presence. However, all seem to point towards systems that will be more complex due to issues such as the tighter integration with software and the interactions with humans-in-the-loop. These ships will likely be completely different compared to conventional ones, which puts into question the usefulness of our current understanding of risk and how it is managed for ensuring safe operation. By a critical review of the literature, this paper highlights the need for advanced methods that will combine machine learning and simulation for dynamically assessing risk in a life cycle context and effectively transferring acquired operational knowledge back to the design phase. The main objective of this paper is to describe a novel life cycle risk framework for developing algorithms inspired by the biological immune system, which provides lifetime protection from harmful pathogens. This framework can be used to construct machine learning algorithms for dynamic risk monitoring and adaptive risk control that address different types of risk, while enabling faster future response to previously unencountered risks and operational feedback to design through learning. We demonstrate the feasibility of our approach in a specific maritime context with a case study on collision risk identification. Considering the lack of experience for autonomous ships, the benefit of our immune-inspired approach is that it departs from the classic risk scenario concept and that it does not rely on safety performance data for identifying risk factors and training the algorithms. We suggest this framework is particularly suitable for autonomous ships with high levels of autonomy, although applicable to conventional ships as well, as it can contribute to empowering them with risk awareness and the capability to deal with any risk environment.

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Section: Life Cycle Risk Assessment For Marine Systemsmentioning

confidence: 99%

Developing next generation marine risk analysis for ships: Bio-inspiration for building immunity

Ventikos

Louzis

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…Intelligent mines are trying to introduce big data analysis and other technologies, with the key equipment of the comprehensive release working face as the research object. It forms the multi-source heterogeneous big data-driven intelligent identification of the fault state of the comprehensive coal mine equipment and the fault prediction technology system through multi-source heterogeneous data collection and the cleaning and distribution storage [2], the state identification of the equipment of the comprehensive working face [3][4][5], and the quantitative fault diagnosis and state 2 of 16 degradation trend prediction of the key equipment [6,7]. In addition, it provides technical support for the guarantee of the safe and efficient production of the intelligent comprehensive working face and the reasonable maintenance of the equipment.…”

Section: Introductionmentioning

confidence: 99%

Research on Large Language Model for Coal Mine Equipment Maintenance Based on Multi-Source Text

Cao,

Xu,

Zhao

et al. 2024

Applied Sciences

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The efficient management and utilization of coal mine equipment maintenance knowledge is an indispensable foundation for advancing the establishment of intelligent mines. This knowledge has problems such as scattered, low sharing, and insufficient management, which restricts the development of coal mine intelligence. For the above-mentioned problems, a large language model for the maintenance of coal mine equipment based on multi-source text (XCoalChat) was proposed to better manage and utilize the existing massive knowledge of coal mine equipment maintenance. The dataset of coal mine equipment maintenance based on ReliableCEMK-Self-Instruction was constructed to obtain a wide and diverse amount of knowledge through sample generation. Aiming at the illusory problem of the large language model, a knowledge graph enhancement method based on the “Coal Mine Equipment Maintenance System—Full Life Cycle—Specification” was proposed to improve the knowledge density. A triple-LoRA fine-tuning mechanism and DPO direct preference optimization method were introduced into the top of the baseline model, which guarantees that XCoalChat can handle multiple Q&A and maintenance decision analysis tasks with limited computing power. Compared with ChatGLM, Bloom, and LLama, the comprehensive assessment of XCoalChat was performed by experiments including coal mine dialog consulting, coal mine professional consulting, and maintenance decision analysis. The results showed that XCoalChat achieved the best response accuracy in professional consulting and maintenance decision analysis; XCoalChat also took the least reasoning time on average. XCoalChat outperformed other mainstream large language models, which verify that XCoalChat is an effective large language model in the field of coal mine equipment maintenance.

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“…Wang et al [18] proposed a new algorithm for the dynamic recognition of coal-rock interface based on adaptive weight optimization and multisensor information fusion. Wang et al [19] proposed an artificial immune algorithm based dynamic health evaluation method to accurately identify the dynamic health states of shearer, so as to reduce the operation troubles and production accidents, and also improve the coal mining efficiency. Jiang et al [20] collected the operation data of the motor, including motor speed, current and voltage, as the monitor signal and used a Bayesian framework based relevance vector machine (RVM) classifier for pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Intelligent Method for Cutting Pick State Recognition of Coal Mining Shearer

Zhang

Meng

Qi³

et al. 2020

IEEE Access

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This paper proposes an intelligent recognition method for shearer cutting state based on deep learning theory, to solve the problems where the picks are prone to various failure forms during the cutting of coal and rock masses by the shearer. The failure will lead to the decline on the stability of the entire machine of the shearer and affect the safety production. Specially, a 1:1 simulation bench is used for simulating underground mining conditions to measure and collect the cutting loads of picks and establish a sample database. Deep learning-based intelligent recognition method is an effective tool that can break away from the dependency of prior knowledge and recognition experience, and sparse. In this paper, a promising deep learning method called sparse filtering is proposed for intelligent recognition of shearer cutting. So sparse filtering is applied to construct an automatic feature extraction model, and softmax regression is adopted as a classifier for cutting pick state recognition. Furthermore, L1/2 regularization term is added to the cost function of sparse filtering to prevent the problem of excessive model training and weights. The proposed method for identifying the cutting status of the shearer can effectively monitor the cutting status of the picker, thereby improving the safety and stability of the cutting of the shearer and promote the coal mining efficiency. INDEX TERMS Shearer pick; sparse filtering; softmax regression; state recognition. K. Zhang et al.: An Unsupervised Intelligent Method for cutting pick state recognition

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A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

Cited by 5 publications

References 32 publications

Developing next generation marine risk analysis for ships: Bio-inspiration for building immunity

Developing next generation marine risk analysis for ships: Bio-inspiration for building immunity

Research on Large Language Model for Coal Mine Equipment Maintenance Based on Multi-Source Text

An Unsupervised Intelligent Method for Cutting Pick State Recognition of Coal Mining Shearer

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