Psychomotor Performance Monitoring System in the Context of Fatigue and Accident Prevention

AbstractPerfecting modern design of technical objects highlights the long-known truth that the human is the most unreliable link in the human-technical object system. However, this is a superficial finding not taking into account the root cause of human error – non-ergonomic working conditions. The desire to improve this situation by increasingly including ergonomic requirements, such as in the design of equipment interfaces, brings significant results. Suitable reduction of fatigue of varying nature, in many cases leads to a reduction in the number of errors among equipment operators. The system model assumes the possibility of a verification of psychomotor status in the workplace directly on the job, not relying on the test results to resume work and only allowing the diagnosis of an undesirable condition. The model is based on, among others, Fitts’ Law. The testing software is a form of web application. Each user has an account on which the results are collected and form the basis for drawing conclusions about the state of fatigue. It is necessary therefore to assume that for dedicated positions there is no risk of distraction by the need for an additional device. Time spent on the task cannot be reduced by the tasks resulting from work. The paradigmatic example of the application of this method can be demonstrated in a study of urban transport vehicles before leaving the initial stop. A study of psychomotor skills can be used as an alternative to the fairly common in some countries testing of alcohol content in exhaled air. There are also breathalyzers integrated in such a way with control of the vehicle, that it is only after the verification of sobriety that one can start the vehicle. There are also no reasons that this check cannot warrant a short psychomotor test

Analysis of Haul Truck- Related Fatalities and Injuries in Surface Coal Mining in West Virginia

Trucks are the primary means of haulage in surface coal, metal, and nonmetal mining operations. The number of fatal accidents involving trucks is higher when compared to all other mining equipment. The Mine Safety and Health Administration (MSHA) reports that 137 fatalities were haul truck- related in the United States between 1995 and 2011. A total of 12 truck-related accidents, including 13 fatalities, were recorded in surface coal mining operations in West Virginia (WV) during this period. The objectives of this research were to (i) analyze the root causes of these accidents, and (ii) develop effective intervention strategies to eliminate these fatalities. The Fault Tree Analysis (FTA) technique was used to systematically analyze truck related fatalities. Data on truck-related injury accidents in West Virginia surface coal mines during 2012 and 2013 were also analyzed in this study. Results of the study indicate that inadequate or improper pre-operational check and poor maintenance of trucks were the two most common root causes of these accidents. A total of eight accidents occurred on haul roads, while 10 accidents occurred while the trucks were moving forward. The two most violated provisions of Code of Federal Regulations were 30 CFR§77.404 - Machinery and equipment; operation and maintenance (six times), and 30 CFR§77.1606 - Loading and haulage equipment; inspection and maintenance (five times).;A total of 223 reported injuries were recorded at West Virginia surface coal mines. With the exception of two missing data, a total of 178 accidents were equipment-related and 43 accidents occurred without equipment being involved. The equipment categories accounting for the most number of injuries were: truck (57 times) and bulldozer/dozer/crawler tractor (43 times). The majority of the truck-related injuries occurred within the worker\u27s first five years at the mine and within the first five years at their current job title. Workers between ages 25 and 39 had the greatest percentage of injuries. Most injuries were recorded during Section I (6:00 a.m. - 2:00 p.m.), and the fall season has the greatest number of truck-related injuries of all four seasons. Regarding the nature of injury, sprains and strains made up about 32%, topping all other types of injuries. The most commonly injured body part in truck-related injuries was the Multiple parts. .;A two-pronged approach to accident prevention was used: one that is fundamental and traditional (safety regulations, training and education, and engineering of the work environment); and one that is innovative and creative (e.g., applying technological advances to better control and eliminate the root causes of accidents). Suggestions for improving current training and education system were proposed, and recommendations were provided on improving the safety of mine working conditions, specifically safety conditions on haul roads, dump sites, and loading areas. Currently available technologies that can help prevent haul truck-related fatal accidents were also discussed. The results of this research may be used by mine personnel to help create safer working conditions and decrease truck-related fatalities and injuries in surface coal mining

A stochastic method for representation, modelling and fusion of excavated material in mining

The ability to safely and economically extract raw materials such as iron ore from a greater number of remote, isolated and possibly dangerous locations will become more pressing over the coming decades as easily accessible deposits become depleted. An autonomous mining system has the potential to make the mining process more efficient, predictable and safe under these changing conditions. One of the key parts of the mining process is the estimation and tracking of bulk material through the mining production chain. Current state-of-the-art tracking and estimation systems use a deterministic representation for bulk material. This is problematic for wide-scale automation of mine processes as there is no measurement of the uncertainty in the estimates provided. A probabilistic representation is critical for autonomous systems to correctly interpret and fuse the available data in order to make the most informed decision given the available information without human intervention. This thesis investigates whether bulk material properties can be represented probabilistically through a mining production chain to provide statistically consistent estimates of the material at each stage of the production chain. Experiments and methods within this thesis focus on the load-haul-dump cycle. The development of a representation of bulk material using lumped masses is presented. A method for tracking and estimation of these lumped masses within the mining production chain using an 'Augmented State Kalman Filter' (ASKF) is developed. The method ensures that the fusion of new information at different stages will provide statistically consistent estimates of the lumped mass. There is a particular focus on the feasibility and practicality of implementing a solution on a production mine site given the current sensing technology available and how it can be adapted for use within the developed estimation system (with particular focus on remote sensing and volume estimation)

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation