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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.

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.

Idlers are essential to conveyor systems, as well as supporting and guiding belts to ensure production efficiency. Proper idler maintenance prevents failures, reduces downtime, cuts costs, and improves reliability. Most studies on idler fault detection rely on supervised methods, which depend on large labelled datasets for training. However, acquiring such labelled data is often challenging in industrial environments due to the rarity of faults and the labour-intensive nature of the labelling process. To address this, we propose the chroma-augmented semi-supervised anomaly detection (CASSAD) method, designed to perform effectively with limited labelled data. At the core of CASSAD is the one-class SVM (OC-SVM), a model specifically developed for anomaly detection in cases where labelled anomalies are scarce. We also compare CASSAD’s performance with other common models like the local outlier factor (LOF) and isolation forest (iForest), evaluating each with the area under the curve (AUC) to assess their ability to distinguish between normal and anomalous data. CASSAD introduces chroma features, such as chroma energy normalised statistics (CENS), the constant-Q transform (CQT), and the chroma short-time Fourier transform (STFT), enhanced through filtering to capture rich harmonic information from idler sounds. To reduce feature complexity, we utilize the mean and standard deviation (std) across chroma features. The dataset is further augmented using additive white Gaussian noise (AWGN). Testing on an industrial dataset of idler sounds, CASSAD achieved an AUC of 96% and an accuracy of 91%, surpassing a baseline autoencoder and other traditional models. These results demonstrate the model’s robustness in detecting anomalies with minimal dependence on labelled data, offering a practical solution for industries with limited labelled datasets.

This study investigates switching sequences in AC-AC converters for conveyor systems. The research explores commutation techniques, power quality concerns, and system performance. It aims to understand control strategies that impact power factor and stability. Safe commutation is crucial for industrial applications to ensure efficient operations. Various converter technologies are analyzed to optimize energy efficiency and reliability. The study focuses on minimizing harmonic distortion through effective switching approaches. Fault scenarios are evaluated to assess the converter response under fluctuating conditions. Protection mechanisms are also discussed for improving system safety and voltage stability. The findings contribute to enhancing the performance of industrial conveyor applications. Harmonic disturbances significantly affect power quality in AC-AC conversion systems. Proper switching techniques must be implemented to reduce electrical interference issues. The research highlights the importance of reliable commutation for operational continuity. Industrial systems require optimized control to maintain efficiency under variable loads. Fault conditions influence power stability and overall system functionality. Robust switching schemes are essential to minimizing operational risks and failures. The study findings provide insights into future advancements in AC-AC converters. It supports engineers in developing more effective industrial power management solutions. The research emphasizes innovative strategies for improving safety and efficiency. Ensuring stable power transmission is critical for the reliability of conveyor systems. The study provides contribution to advancing industrial power electronics and control methodologies.

PurposeThe purpose of this paper is to develop a framework for the safety performance measurement of belt conveyor systems.Design/methodology/approachA structural methodology of graph theory and matrix approach is used for developing a framework for safety performance measurement of belt conveyor systems.FindingsThe development of a framework for safety performance measurement of belt conveyor systems is essential for ensuring plant safety. For this, safety performance factors, including design and operating contextual factors of belt conveyor systems, are identified. The factors along with their interrelations are modeled using digraph. An equivalent matrix of the digraph provided safety performance function (SPF) of belt conveyor systems, leading to the development of a safety performance index (SPI).Practical implicationsThe developed framework will enable the designers for evaluating and comparing alternative designs of conveyor systems from the safety viewpoint. The plant operators can make inferences from the SPI to identify the weak contextual factors in the plant and develop action plans for its mitigation.Originality/valueThe paper is novel and employs graph theory and matrix approach for safety performance measurement. The methodology helps in the quantitative evaluation of the safety performance of belt conveyor systems.

Industrial conveyor systems commonly use steel flight bars, which can account for nearly 50% of the total system mass and significantly affect energy consumption. This study investigates epoxy matrix composites reinforced with glass, carbon, and Kevlar fibers as lightweight alternatives to steel flight bars. A multiscale analytical approach combining micromechanics, Classical Laminate Theory (CLT), and ply-level failure criteria is applied to evaluate the structural response under an industrial bending moment of 342.02 N·m. Tensile tests on vacuum-infused woven glass/epoxy laminates are used to validate micromechanical assumptions and calibrate elastic properties. Ply-wise analysis shows that carbon/epoxy laminates exhibit the lowest longitudinal stresses (≈43 MPa), followed by Kevlar/epoxy (≈53 MPa) and glass/epoxy (≈95 MPa), all well below their respective strength limits. Replacing steel flight bars (4.64 t) with composite alternatives reduces the moving mass to 0.68–0.82 t, corresponding to an 82–85% reduction. This mass reduction significantly lowers the required mechanical power, resulting in an estimated annual energy saving of R$ 8812.80 under continuous operation. Overall, the results demonstrate that polymer-matrix composite flight bars are structurally safe and energetically advantageous, with carbon/epoxy providing the highest mechanical efficiency.

Industrial electric drives account for a dominant share of electricity consumption in manufacturing, making their optimal configuration a critical factor for both sustainability and cost reduction. Traditional design approaches based on prototyping and empirical testing are often costly and insufficient for systematically exploring alternative configurations. This study introduces an integrated computational framework that combines digital twin (DT) modeling and virtual commissioning (VC) to enable energy-aware configuration of industrial electric drive systems at early design stages. The methodology employs parameterized component models derived from manufacturer catalog data, implemented in a commercial simulation environment and integrated into an industrial-grade VC platform. Validation is performed on two conveyor-based testbeds, enabling systematic comparison of simulation outputs with physical measurements. The results demonstrate predictive accuracy sufficient to quantify trade-offs in energy consumption, losses, and efficiency across different vendor solutions. Case studies involving belt and strap conveyors highlighted how the framework supports vendor-neutral decision making, revealing nonintuitive optimization trade-offs between minimizing energy consumption and maximizing efficiency. The proposed framework advances sustainable automation by embedding energy analysis directly into commissioning workflows, offering reproducible, scalable, and cross-domain applicability. Its modular design supports transfer to sectors such as renewable energy, transportation, and biomedical mechatronics, where energy efficiency is equally decisive.

A two‐degree‐of‐freedom nonlinear moving belt system is investigated, which considers the possibility of two oscillators sticking or sliding on the moving belt simultaneously. The different regions and boundaries for this system are defined according to the discontinuity due to the friction. The existence conditions and stability conditions of the equilibrium point are deduced analytically. The one‐parameter bifurcation diagram of the sliding segment time and the overshooting segment time with respect to the velocity of the moving belt is presented in order to understand the codimension‐one sliding bifurcation of the system more intuitively. Then, a two‐parameter numerical continuation for sliding bifurcation is carried out, and some intersection points of sliding bifurcation curves are obtained. These codimension‐two bifurcation points are classified into two types: Type‐I codimension‐two bifurcation points and Type‐II codimension‐two bifurcation points. The latter has rarely been reported in the literature. The unfolding of the dynamics of the system around the codimension‐two bifurcation points is obtained. The results of numerical simulation show that the system has rich and complex dynamic behaviors.

The article considers the possibilities of automated use of robotic equipment in order to form an infrastructure for moving goods at enterprises. Areas of application of algorithmic programming languages of object-oriented type in robotics are investigated. The algorithm of operation of a transport vehicle, the movement, which is based on the recognition of the line of motion, describing the route of movement, is presented. The analysis of the peculiarities of the implementation of such problems with the use of OpenCv software library was carried out. The structure of the vehicle is proposed, in particular: its driving mechanisms, control scheme, engines and wheelbase. Further development was made to the algorithms for the management of crawler lorries and the ways of their program realization in various spheres of entrepreneurial activity, where there is a need for the transfer of cargoes in the ordinary areas (construction sites, forest lands, open warehouses, airports, etc.). Based on the proposals for creating a cargo robot that can be moved according to a given route, the model of control system of conveyor systems, which solve the issues of automation of technological processes in the part of the addition of conveyor systems, is presented. The analysis of literary sources describing the necessity of creating mobile conveyor systems in production, which enables to quickly re-equip production processes to unforeseen needs, was carried out.

This study investigates the influence of idler roller diameter on indentation rolling resistance and idler rotating resistance in belt conveying systems, crucial for long-distance bulk material transport. It encompasses the impact on grease-lubricated rolling bearings, grease-filled labyrinth seals, and lip seals, with the aim of optimizing energy consumption. Experimental devices were used to refine predictive models, demonstrating that larger idler rollers reduce both resistances, leading to a 40% to 55% efficiency improvement. The study offers a detailed breakdown of friction losses under various operating conditions and provides valuable insights for lubricant selection and system enhancement, highlighting the significance of idler roller diameter in reducing energy costs and enhancing system performance.