Masoumeh Khoshkerdar, Reza Saeedi, Amin Bagheri, Mohammad Hajartabar, Mohammad Darvishi, Reza Gholamnia,
Volume 14, Issue 1 (3-2024)
Abstract
Introduction: The goal of this study is to investigate how the development of technology has affected the industry (especially the mining industry). For this purpose, this paper examines the impact of intelligent mining machinery systems, including tire pressure monitoring systems (TPMS), dispatching systems, and vehicle health monitoring systems (VHMS), on health, safety, and environmental parameters and preventative maintenance.
Material and Methods: This study is descriptive-analytical research that was conducted between time intervals before and after employing the intelligent mining machinery systems. Initially, parameters were identified using the Delphi method. These parameters include human accidents, equipment accidents, environmental incidents, warnings and fines in the domains of health, safety, and the environment, tire usage parameters, the shelf life of the tire, oil overfill, fuel consumption, failure rate, mean time between failures, and preventive maintenance compliance schedules in the domain of preventative maintenance. The effectiveness of using these systems was then assessed by comparing the state of the specified parameters before and after the introduction of the intelligent mining machinery systems.
Results: The findings of this research indicate that using intelligent mining machinery systems will decrease equipment accidents by 33.3%, extend the useful life of tires by 7.1%, reduce fuel consumption by 14.6%, cut the mean time required to repair by 25.5%, and enhance preventive maintenance compliance schedules by 5.7%.
The findings showed the effectiveness of the use of intelligent systems of mining machines was obtained as follows: reduction of equipment accidents by 33.3%, increasing the useful life of tires by 7.1%, reducing fuel consumption by 14.6%, reducing the average downtime of the car for repair by 25.5% and increasing compliance with the maintenance program by 5.7%.
Conclusion: Utilizing intelligent mining machinery systems might have a positive impact on the safety of machines, reduce negative environmental effects like fuel consumption, and improve the maintenance of heavy machinery, which would lead to better mining conditions and lower costs.
Hassan Mehridiz, Mohamad Sadegh Ghasemi Ghasemi, Hassan Saeedi, Mahsa Varmazyar, Ehsan Garosi,
Volume 14, Issue 2 (6-2024)
Abstract
Introduction: Lifting loads in awkward postures is a main cause of low back musculoskeletal disorders. In this context, researchers have used various indicators to determine the relationship between biomechanical variables and the risk of these disorders. This study aimed to investigate the correlation between plantar pressure distribution and the values of UTAH back-compressive forces (BCF) and lifting index (LI) during symmetrical load-lifting tasks.
Material and Methods: Thirteen healthy men, aged 25 to 35, took part in this study. The participants were instructed to symmetrically lift loads weighing 7.5 kg and 15 kg in 15 different postures, considering three horizontal distances (A, B, C) and five different heights (1-5). Pressure on the foot soles was recorded using 16 force-sensitive resistors (FSR) corresponding to eight anatomical areas on each foot. The BCF and LI were also calculated using the UTAH method and the NIOSH equation, respectively. Statistical analysis was performed using SPSS (version 21) software.
Results: Based on the results, when the load was closest to the body (A1-A5), the highest pressure was recorded in the heel and the 4th and 5th metatarsal of both feet. In lifting a load of 15 kg in the A2, B1, B2, C1, C2 postures and lifting a load of 7.5 kg in the C2 posture, the average BCF exceeded 700 pounds. The LI was greater than 1 for specific postures (B1, B2, B4, B5, C1-C5) at 15 kg and (C1, C2, C4, C5) at 7.5 kg load-lifting. During the 7.5 kg and 15 kg load-lifting, there was a significant correlation between the plantar pressure and the values of LI and UTAH (p-values < 0.05) in most postures.
Conclusion: The results showed a significant correlation between plantar pressure distribution and load-lifting postures. The study findings, which identify risk levels associated with lifting postures, lay the groundwork for future research aimed at categorizing safe and unsafe plantar pressure patterns.
