Conveyor Idlers Research Articles

Belt Conveyor Idlers Fault Detection Using Acoustic Analysis and Deep Learning Algorithm With the YAMNet Pretrained Network

Belt Conveyor Idlers Fault Detection Using Acoustic Analysis and Deep Learning Algorithm With the YAMNet Pretrained Network

Fault diagnosis of belt conveyor idlers based on gradient boosting decision tree

Fault diagnosis of belt conveyor idlers based on gradient boosting decision tree

A Review of Fault Detecting Devices for Belt Conveyor Idlers

The shift towards automated inspection methods represents considerable progress in conveyor system maintenance, enhancing safety and efficiency while posing challenges in data analysis and implementation costs. This study critically analyses sensor technologies and inspection methods for detecting faults in conveyor belt idlers, highlighting their essential role in preserving the operational integrity of industrial conveyor systems. By synthesizing various research findings, the study assesses the effectiveness of different sensor devices in identifying defects, including built-in sensors, fixed sensor options like acoustic, ultrasonic sensors, cameras, accelerometers, and Distributed Optical Fibre Sensors (DOFS), as well as mobile sensor systems. Our findings emphasize the accuracy of robot-based systems in identifying bearing defects, the comprehensive coverage provided by drones for medium-scale inspections, the constant monitoring offered by integrated idler sensors, and the ability of fixed sensors to detect mechanical faults despite environmental challenges. This research adds to the ongoing discussion on enhancing conveyor system dependability through technological advancements, providing insights into potential future developments that could further refine maintenance strategies in the sector.

Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks

The belt conveyor (BC) is the main means of horizontal transportation of bulk materials at mining sites. The sudden fault in BC modules may cause unexpected stops in production lines. With the increasing number of applications of inspection mobile robots in condition monitoring (CM) of industrial infrastructure in hazardous environments, in this article we introduce an image processing pipeline for automatic segmentation of thermal defects in thermal images captured from BC idlers using a mobile robot. This study follows the fact that CM of idler temperature is an important task for preventing sudden breakdowns in BC system networks. We compared the performance of three different types of U-Net-based convolutional neural network architectures for the identification of thermal anomalies using a small number of hand-labeled thermal images. Experiments on the test data set showed that the attention residual U-Net with binary cross entropy as the loss function handled the semantic segmentation problem better than our previous research and other studied U-Net variations.

Belt conveyor idler fault diagnosis method based on multi-scale feature fusion and residual mask convolution attention

Existing idler fault diagnosis methods have problems in failing to fully obtain global context information and providing poor diagnostic accuracy. To address these problems, this paper investigates a new method for diagnosing faults in belt conveyor idlers, based on analysis of their acoustic signals. The method is also applied to existing databases of bearing fault data. Firstly, an eight-element microphone array sound signal collector is designed to suppress environmental noise and raise the signal-to-noise ratio of the idler sound signal. Secondly, a multi-scale feature fusion (MSFF) module is constructed to learn complementary information between features at different scales. Then, a residual mask convolutional attention (MCA) module is designed to raise the modelling capability of local features and global contextual information. Finally, the structure of the ResNet-18 network is optimised to improve model fitting performance. Experimental results on self-made and public datasets show that the suggested method outperforms other comparative methods, achieving real-time accurate detection and classification of belt conveyor idler faults and typical bearing faults.

Prospects of in-belt IMU sensors application for fault detection in mining conveyors

The belt conveyors of high power are the main transport in the industry, especially for mining enterprises. Since the total length of conveyors in the mines reaches several kilometres, the inspection of all idlers in harsh conditions is a challenge for maintenance personnel because the jamming of supporting idlers with damaged bearings causes belt overheating and subsequent fire. Conveyor belt breaking is also a serious trouble during continuous operation. In this research, the authors based on preliminary measurements are aiming to show that fault detection in belt conveyor idlers is quite possible to be conducted using a single or multiple small-size IMU (or other type thin sensors) placed directly on the belt or inside it. The information available from the IMU sensor is discussed in this paper. Transient signals are associated with the sensor passing each idler. Some methods for signals processing and possible diagnostic features extraction are presented.

A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models.

Due to increasing demands for ensuring the safety and reliability of a system, fault detection (FD) has received considerable attention in modern industries to monitor their machines. Bulk materials are transported worldwide using belt conveyors as an essential transport system. The majority of conveyor components are monitored continuously to ensure their reliability, but idlers remain a challenge to monitor due to the large number of idlers (rollers) distributed throughout the working environment. These idlers are prone to external noises or disturbances that cause a failure in the underlying system operations. The research community has begun using machine learning (ML) to detect idler's defects to assist industries in responding to failures on time. Vibration and acoustic measurements are commonly employed to monitor the condition of idlers. However, there has been no comprehensive review of FD for belt conveyor idlers. This paper presents a recent review of acoustic and vibration signal-based fault detection for belt conveyor idlers using ML models. It also discusses major steps in the approaches, such as data collection, signal processing, feature extraction and selection, and ML model construction. Additionally, the paper provides an overview of the main components of belt conveyor systems, sources of defects in idlers, and a brief introduction to ML models. Finally, it highlights critical open challenges and provides future research directions.

A Dynamic Self-Attention-Based Fault Diagnosis Method for Belt Conveyor Idlers

Idlers are typical rotating parts of a belt conveyor carrying the conveyor belt and materials. The complex operating noise and unstable features lead to poor accuracy of sound-based idler fault diagnosis. This paper proposes a fault diagnosis method for belt conveyor idlers based on Transformer’s dynamic self-attention (DSA). Firstly, the A-weighted time-frequency spectrum of the idler sound is extracted as the input. Secondly, based on the DSA block, the multi-frequency cross-correlation DSA algorithm is designed to extract the cross-correlation features between different frequency bands in the input feature map, and the global DSA algorithm is applied to perceive and enhance the global correlation features in parallel. Finally, the cross-correlation and global correlation features are concatenated and linearly projected into a fault-type space to diagnose typical bearing and roller faults of idlers. The method makes full use of the relevant information scattered in different frequency bands of the idler running sound under complex working conditions and reduces the negative effect of the strong running noise on the extraction of weak fault features. Experimental results show that the fault diagnosis accuracy is 94.6% and the latency is 27.8 ms.

Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary Classification with CNN

Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary Classification with CNN

Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary Classification with CNN.

Mechanical industrial infrastructures in mining sites must be monitored regularly. Conveyor systems are mechanical systems that are commonly used for safe and efficient transportation of bulk goods in mines. Regular inspection of conveyor systems is a challenging task for mining enterprises, as conveyor systems' lengths can reach tens of kilometers, where several thousand idlers need to be monitored. Considering the harsh environmental conditions that can affect human health, manual inspection of conveyor systems can be extremely difficult. Hence, the authors proposed an automatic robotics-based inspection for condition monitoring of belt conveyor idlers using infrared images, instead of vibrations and acoustic signals that are commonly used for condition monitoring applications. The first step in the whole process is to segment the overheated idlers from the complex background. However, classical image segmentation techniques do not always deliver accurate results in the detection of target in infrared images with complex backgrounds. For improving the quality of captured infrared images, preprocessing stages are introduced. Afterward, an anomaly detection method based on an outlier detection technique is applied to the preprocessed image for the segmentation of hotspots. Due to the presence of different thermal sources in mining sites that can be captured and wrongly identified as overheated idlers, in this research, we address the overheated idler detection process as an image binary classification task. For this reason, a Convolutional Neural Network (CNN) was used for the binary classification of the segmented thermal images. The accuracy of the proposed condition monitoring technique was compared with our previous research. The metrics for the previous methodology reach a precision of 0.4590 and an F1 score of 0.6292. The metrics for the proposed method reach a precision of 0.9740 and an F1 score of 0.9782. The proposed classification method considerably improved our previous results in terms of the true identification of overheated idlers in the presence of complex backgrounds.

Design of an Infrared Image Processing Pipeline for Robotic Inspection of Conveyor Systems in Opencast Mining Sites

Conveying systems play an essential role in the continuous horizontal transportation of raw materials in mining sites. Regular inspections of conveyor system structures and their components, especially idlers, are essential for proper maintenance. Traditional inspection methods are labor-intensive and hazardous; therefore, robot-based thermography can be considered a quality assessment tool for the precise detection and localization of overheated idlers in opencast mining sites. This paper proposes an infrared image processing pipeline for the automatic detection and analysis of overheated idlers. The proposed image processing pipeline can be used for the identification of significant temperature anomalies such as hotspots and hot areas in infrared images. For the identification of such defects in idlers, firstly, the histogram of captured infrared images was analyzed and improved through the pre-processing stages. Afterward, the location of thermal anomalies in infrared images was extracted. Finally, for the validation of segmentation results, the shapes and locations of segmented hot spots were compared with RGB images that were synchronized by captured infrared images. A quantitative evaluation of the proposed method for the condition monitoring of belt conveyor idlers in an open-cast mining site shows the applicability of our approach.

Acoustic-based diagnostics of belt conveyor idlers in real life mining conditions by mobile inspection robot

Acoustic-based diagnostics of belt conveyor idlers in real life mining conditions by mobile inspection robot

Measurement of idlers rotation speed in belt conveyors based on image data analysis for diagnostic purposes

It is a challenge for mining enterprises to diagnose the belt conveyors, which length can be tens of kilometres and the number of idlers reaches several thousand. All of them require inspection, which is infeasible to perform using monitoring sensors, such as accelerometers. The authors proposed a novel method for the rotational speed measurement of idlers based on image data analysis. The relation is assumed of the rotation speed decrease due to internal defects. The procedure of visual data processing is verified on the real conveyor idlers. The remote sensing method can be applied in mobile inspection robots such as UAVs or UGVs as well as for manual camera recordings. The accuracy of the proposed method is 0.13-0.67% error depending on the speed range, which is provided by the standard sampling rates (30-60 FPS) and video resolution (1280 x 720 px). Recommendations are formulated for method implementation in practice.

Inspection Robotic UGV Platform and the Procedure for an Acoustic Signal-Based Fault Detection in Belt Conveyor Idler

Belt conveyors are commonly used for the transportation of bulk materials. The most characteristic design feature is the fact that thousands of idlers are supporting the moving belt. One of the critical elements of the idler is the rolling element bearing, which requires monitoring and diagnostics to prevent potential failure. Due to the number of idlers to be monitored, the size of the conveyor, and the risk of accident when dealing with rotating elements and moving belts, monitoring of all idlers (i.e., using vibration sensors) is impractical regarding scale and connectivity. Hence, an inspection robot is proposed to capture acoustic signals instead of vibrations commonly used in condition monitoring. Then, signal processing techniques are used for signal pre-processing and analysis to check the condition of the idler. It has been found that even if the damage signature is identifiable in the captured signal, it is hard to automatically detect the fault in some cases due to sound disturbances caused by contact of the belt joint and idler coating. Classical techniques based on impulsiveness may fail in such a case, moreover, they indicate damage even if idlers are in good condition. The application of the inspection robot can “replace” the classical measurement done by maintenance staff, which can improve the safety during the inspection. In this paper, the authors show that damage detection in bearings installed in belt conveyor idlers using acoustic signals is possible, even in the presence of a significant amount of background noise. Influence of the sound disturbance due to the belt joint can be minimized by appropriate signal processing methods.

Research on the fault analysis method of belt conveyor idlers based on sound and thermal infrared image features

The large number, scattered distribution, and complex working environment of idlers makes their faults challenging to detect. In this paper, a fault analysis method of belt conveyor idlers based on sound and thermal infrared image (TII) features is proposed. According to 18 classes of idler sound and TII data, the time-domain (TD) features of sound signals are analysed using statistical methods, the frequency-domain (FD) and time-frequency-domain (TFD) features of sound signals are analysed with the quantization and dimension reduction method based on Fisher’s linear discriminant, and the TII features are analysed using statistical methods. The analysis results show that final and catastrophic faults can be detected by using FD features of idler sound and TII temperature rise of the idler outer load area and shaft end, and TFD features of idler sound signals can be used to detect typical bearing defects, which features high reliability, low cost, and easy implementation.

Research on tightness loss of belt conveyor's idlers and its impact on the temperature increase of the bearing assemblies

Research on tightness loss of belt conveyor's idlers and its impact on the temperature increase of the bearing assemblies

Technological Innovation on Conveyor Idlers

Purpose of research: • Traditionally conveyor idlers are made of steel or rubber lagged steel or PVC sleeved. • Fertiliser & Chemicals are hygroscopic and tends to absorb moisture from the atmosphere • This absorption accelerates corrosion of Steel components of the conveyors affecting the life of the idlers and the conveyor belt. Frequent replacement of idlers and the conveyor belt affects the productivity • Most of the bulk material transportation is through sea where the humidity is very high. • Steel idlers are heavier consuming more energy Innovation: • Corrosive surface damages the belt covers • Finely finished surfaces of ball bearings are subjected to corrosion spreading from steel shaft resulting in premature failure of the bearings • Lower energy required for rotation of lighter idlers These gave birth to “HPPE Polymer Idlers” which is light in weight, non-corrosive and non-metallic. Benefits in energy saving, costs saving, increased belt life and improved productivity.

Acoustic signal based fault detection on belt conveyor idlers using machine learning

Belt conveyor systems are widely utilized in transportation applications. This research aims to achieve fault detection on belt conveyor idlers with an acoustic signal based method. The presented novel method uses Mel Frequency Cepstrum Coefficients and Gradient Boost Decision Tree for feature extraction and classification. Thirteen Mel Frequency Cepstrum Coefficients are extracted from acquired sound signal as features. A Gradient Boost Decision Tree model is developed and trained. After training, the model is applied to a testing dataset. Results show that the trained model can achieve diagnosis accuracy of 94.53%, as well as recall rate up to 99.7%. This study verifies the proposed method for acoustic signal based fault detection of belt conveyor idlers.

Limit angle of the belt conveyor inclination at transporting lumpy loads

One of the ways to reduce capital costs and shorten length of conveyor transport routes is to increase angle of the conveyor inclination. As practice shows, limit inclination angle of general-purpose belt conveyors transporting bulk loads is 18°- 22°, while angle of natural slope of ore and coal is 40° and 35°, respectively. Such a difference between the angles of bulk load slope and limit inclination angle of the belt conveyor transporting the bulk loads is explained by the belt vibration impacting on the state of the transported load during its moving along the belt conveyor idlers. When exposed to vibrations, the solid-yielding state of the granular medium passes into a free-dispersed state at which inertial forces of the particles exceed the forces of internal coulomb friction between the particles. Moreover, as experimental studies have shown, granular medium moves relative to the conveyor belt at anglesof conveyor inclination of 18°–22°, i.e. limit angle of inclination of the conveyor belt transporting bulk load does not exceed 18°–22°. Today, limit angle of inclination of the conveyor belt transporting bulk loads is not studied well. In this research, the authors tried to determine limit angle of inclination of the conveyor belt transporting bulk loads by considering different types of the idlers: with rigid line, damping idlers and suspension idler with rope line. Limit angle of the conveyor belt inclination was determined on the basis of the change in momentum theorem applied to the lump during its moving over the idler and with the assumption that a lump, while passing over the idler, began sliding along the conveyor belt. As a result, analytical dependences of limit angle of the conveyor belt inclination on the conveyor parameters (belt tension and speed), parameters of idlers (distance between them), length of the bulk load and coefficient of friction of the lump against the conveyor belt were obtained. The studies of the obtained dependences showed that belt conveyors with damping idlers featured the widest limit angle of inclination, and inclination angel of the belt conveyors with suspension idlers had the least value. In addition, it was found that with increase of the conveyor belt speed and weight of the transported lumps limit angle of the conveyor inclination decreased and increased with increase of the belt tension. Further, critical speed of the conveyor belt was determined at which piece of load began sliding along the conveyor belt at any angle of the conveyor inclination.

Experimental investigation of conveyor idlers operational characteristics

The efficiency of thermal power plants significantly depends on the efficiency of belt conveyor systems. Conveyor idlers, as key parts of belt conveyors, are often the main cause of bulk material transportation systems failures. Testing the operational characteristics of conveyor idlers in laboratory and exploitation conditions is a prerequisite for any progress in the field of increasing energy efficiency of conveyor systems. This paper describes an analysis and discussion of testing results for different types of conveyor idlers, in terms of their performances under the act of predefined radial load. Tests were conducted on a new conveyor idler’s testing machine developed at the University of Belgrade - Faculty of Mechanical Engineering. The presented tests are performed in order to determine the quality of carrying and return conveyor idlers. The obtained results will help in solving the key problems in the critical areas of conveyor systems and will increase their energy efficiency. Finally, the expected results can potentially reduce the financial and energy losses which conveyor idlers cause in belt conveyor systems, as well as in the whole system of thermal power plants.