Fault diagnosis method of belt conveyor idler based on sound signal

Zhang, Yahui; Li, Siyan; Li, Aimin; Zhang, Gaoxiang; Wu, Mingzhuang

doi:10.1007/s12206-022-1208-1

Cited by 10 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, this method can only detect the sound caused by the internal fault of the roller bearing and the loosening of the roller frame, so as to realize the fault detection and judgment of the roller [ 11 ]. Reference [ 12 ] uses the MFCC feature of multi-frame fusion to increase the effect of roller fault detection.…”

Section: Related Workmentioning

confidence: 99%

Thermal infrared imaging for conveyor roller fault detection in coal mines

Xiuyu,

Liyong,

Feng

2024

PLoS ONE

View full text Add to dashboard Cite

As a key mechanism of belt conveyor, the health status and working state of its parts have a profound impact on whether the belt conveyor can run normally and safely. In the composition of the standard belt conveyor, the number of rollers is numerous and scattered. At the same time, under the complex environment of the work site, the fault detection of each roller is particularly difficult. In order to solve the above problems, a diagnosis method based on thermal infrared image features is proposed to detect the faults of each roller mechanism in the belt conveyor. Firstly, the position of the idler is identified based on the YOLOv4 identification method, and then the sticking resistance and bearing damage of the idler are detected based on the temperature difference discrimination method. In this paper, the target recognition method based on YOLOv4 is used to identify the position of the roller, and the recognition accuracy is 93.8%, which meets the requirements of the project. The infrared image obtained by the dual-spectrum camera is used to distinguish the fault of the idler in the coal mine. The temperature of the bearing and surface of the normal roller increases rapidly within 10 minutes of operation, and the temperature changes slightly after 10 minutes of operation. The bearing damaged idler has a greater friction effect at the bearing, so the temperature at the bearing rises faster, and there is a temperature difference of about 7°C between the bearing and the normal roller. The surface temperature of the idler in the blocking state is also fast for about 20 minutes, and there will be a temperature difference of about 8°between the surface of the idler and the normal roller. In this paper, it is determined that the temperature rise coefficients of the roller surface and bearing under normal conditions are 24% 28% and 18% 22% respectively. It is determined that the threshold value of the temperature rise coefficient in the blocking state and the damaged state is 30% and 25% respectively, that is, when the surface temperature rise coefficient of the roller is detected to be more than 30%, it is determined that the card resistance fault occurs, when the temperature rise coefficient at the roller bearing is detected > 25%, the bearing damage fault is judged.

show abstract

Section: Related Workmentioning

confidence: 99%

Thermal infrared imaging for conveyor roller fault detection in coal mines

Xiuyu,

Liyong,

Feng

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Neural networks exhibit strong learning and generalization capabilities, enabling automated fault detection in conveyor belt drums based on patterns learned from extensive data. Zhang et al proposed a method for conveyor belt drum fault detection based on audio wavelet packet decomposition and Convolutional Neural Networks (CNN) [5]. They employed CNN to classify the features of drum audio signals across different frequency bands, albeit with relatively low accuracy.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode

Du,

Yu,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

This paper presents a comprehensive study on enhancing the accuracy, real-time performance, and reliability of fault detection in conveyor belt drums. Leveraging insights from two distinct approaches, a novel lightweight network model, YOLOv8n + EMBC + SCC, is proposed. The model integrates the strengths of YOLOv8n in target detection accuracy and speed with innovative modules designed for improved performance. Firstly, the EMBC module, based on DSC high-efficiency convolution, replaces the traditional C2F module in the backbone and neck segments, resulting in a notable 14.5% increase in speed and a 0.7% enhancement in accuracy. Secondly, the SCC efficient convolution module replaces the Conv module in the detection head, further optimizing computational load and model performance, leading to an additional 11.73% increase in speed and a 0.7% improvement in accuracy. Experimental results demonstrate the efficacy of the proposed model, achieving a detection accuracy of 93.4%, surpassing YOLOv8n by 0.9%. Moreover, the model exhibits an improved Frames Per Second (FPS) value of 38.21, representing a 3.56 f/s advancement over YOLOv8n. Heatmap analysis validates the model's superiority in terms of high detection accuracy, precise fault identification, and clear fault localization. This research contributes to the development of a fast, precise, and reliable fault detection system suitable for conveyor belt drum applications, with implications for improving operational efficiency and maintenance practices in industrial settings.

show abstract

Fault diagnosis method of belt conveyor idler based on sound signal

Cited by 10 publications

References 27 publications

Thermal infrared imaging for conveyor roller fault detection in coal mines

Thermal infrared imaging for conveyor roller fault detection in coal mines

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

Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode

Contact Info

Product

Resources

About