M.Tech. (Business Administration, Faculty of Management Sciences), Vaal University of Technology. / The majority of mine health and safety authorities around the world agree that the quality of safety standards is of increasing importance to the mining industry across the world (Kleyn & du Plessis 2016:309). Mining companies in many countries such as New Zealand, (an island country in the south-western Pacific Ocean), Australia, South Africa and China have taken up the challenges of guaranteeing liability and improving performance of the safety and health of their workers, aware that many workers are injured, if not fatally. These incidents result in production loss. This study provides not only an opportunity to evaluate the status of the safety control measures of the work system in a mining company, but also enables management to pinpoint the causes of poor safety performance and implement efforts that ensure safety improvement. The primary objectives of this study were to examine factors influencing the adherence and employee perceptions towards safety control measures in a mining company. Furthermore, the governments in many countries have tried to implement legislation to try to curb the scourge of industrial accidents. Safety disclosures of the annual reports from the Department of Mineral Resources (DMR) of South African mining organisations, discloses 10 major mining accidents that happened in 2015 at Northern Cape mining companies. Six of these accidents occurring from a small mining sector and four from a large mining sector, except previous year’s safety records as detailed in this study. A quantitative approach was adopted for the study. The data were collected using a sample of 200 participants in which a survey questionnaire was administered to permanent mine employees and full time contractors in the mine. A simple sampling technique was used and data were then analysed using the Statistical Package for the Social Science (SPSS) version 25.0 to formulate frequency tables and descriptive analysis graphs. Furthermore, one-way analysis of variance (ANOVA) and t-test were utilised to analyse the data and examine significant differences between employee perceptions and attitudes towards safety control measures, age and length of service (Willemse 2009:118-121). The results reveal that although the mine was considered compliant, with its employees showing a positive attitude towards safety control measures, ANOVA revealed different perceptions of employees based on their age and years of experience. However, no differences were found in relation to gender and occupation. Based on the findings, this study further recommends future studies to be conducted in order to explore the effectiveness of implementing an internal system of self-evaluation as a starting point in any safety improvement process. An effective system of internal self-evaluation will trademark the mining sector internationally and improve workers’ safety by improving effectiveness and assurance of the control measures and the level of control performance criteria. The system should create the awareness of adherence to safety control measures and deal with employee perception towards safety adherence in mining. In addition it should be a system that ensures a structured and standardised approach to learning from incidents and that all necessary steps are followed to safeguard against repeats of incidents and accidents through an effective incident investigation process (Van den Berg 2014:11). The findings of the study revealed that the leadership in the mine has a strong, positive and significant influence on the performance of safety. In this regard, this study recommends that an effective employee engagement system to be developed and that mine managers establish a safety control charter that must be understood by the mine workers, develop a code of ethics that requires ethical and honest behaviour from all employees in order to improve safety performance and learn from these accomplishments. Mine workers will take their cue from the attitude and example displayed by management, therefore, it is recommended that mine management develop an organisational culture, which assigns authority and responsibility to employees and organises and develops employees with direction provided by management that determines the type of culture in that mine. To minimise or reduce the risk of health exposure of each activity as highlighted under Regulation 9 of the Mine Health and Safety Act (29 of 1996), it is recommended that mine manager’s enforce the use of protective equipment. The leadership and human resources, mine workers and all persons who may be affected by the mining activities in the surrounding area of operation need to be aware of the factors that can impact their well-being. The study also presented managers, mine owner and other decision makers within the mining company with important insight on key areas of factors that may require particular attention in order to enhance their operational strategies towards zero harm in the mine.

This study presents a procedure for the estimation of stopping behavior of industrial robots with a trained neural network. This trained network is presented as a single channel in a redundant architecture for safety control applications, where its potential for future integration with an analytical model of robot stopping is discussed. Basic physical relations for simplified articulated manipulators are derived, which motivate a choice of quantities to predict robot stopping behavior and inform the training and testing of a network for prediction of stopping distances and times. Robot stopping behavior is considered in the context of relevant standards ISO 10218-1, ISO/TS 15066 and IS0 13849-1, which inform the definitions for safety related stopping distances and times used in this study. Prior work on the estimation of robot stopping behavior is discussed alongside applications of machine learning to the broader field of industrial robotics, and particularly to the cases of prediction of forward and inverse kinematics with trained networks. A state-driven data collection program is developed to perform repeated stopping experiments for a controlled stop on path within a specified sampling domain. This program is used to collect data for a simulated and real robot system. Special attention is given to the identification of meaningful stopping times, which includes the separation of stopping into pre-deceleration and post-deceleration phases. A definition is provided for stopping of a robot in a safety context, based on the observation that residual motion over short distances (less than 1 mm) and at very low velocities (less than 1 mm/s) is not relevant to robot safety. A network architecture and hyperparameters are developed for the prediction of stopping distances and times for the first three joints of the manipulator without the inclusion of payloads. The result is a dual-network structure, where stopping distance predictions from the distance prediction network serve as inputs to the stopping time prediction network. The networks are validated on their capacity to interpolate and extrapolate predictions of robot stopping behavior in the presence of initial conditions not included in the training and testing data. A method is devised for the calculation of prediction errors for training training, testing and validation data. This method is applied both to interpolation and extrapolation to new initial velocity and positional conditions of the manipulator. In prediction of stopping distances and times, the network is highly successful at interpolation, resulting in comparable or nominally higher errors for the validation data set when compared to the errors for training and testing data. In extrapolation to new initial velocity and positional conditions, notably higher errors in the validation data predictions are observed for the networks considered. Future work in the areas of predictions of stopping behavior with payloads and tooling, further applications to collaborative robotics, analytical models of stopping behavior, inclusion of additional stopping functions, use of explainable AI methods and physics-informed networks are discussed. / Master of Science / As the uses for industrial robots continue to grow and expand, so do the need for robust safety measures to avoid, control, or limit the risks posed to human operators and collaborators. This is exemplified by Isaac Asimov's famous first law of robotics - "A robot may not injure a human being, or, through inaction, allow a human being to come to harm." As applications for industrial robots continue to expand, it is beneficial for robots and human operators to collaborate in work environments without fences. In order to ethically implement such increasingly complex and collaborative industrial robotic systems, the ability to limit robot motion with safety functions in a predictable and reliable way (as outlined by international standards) is paramount. In the event of either a technical failure (due to malfunction of sensors or mechanical hardware) or change in environmental conditions, it is important to be able to stop an industrial robot from any position in a safe and controlled manner. This requires real-time knowledge of the stopping distance and time for the manipulator. To understand stopping distances and times reliability, multiple independent methods can be used and compared to predict stopping behavior. The use of machine learning methods is of particular interest in this context due to their speed of processing and the potential for basis on real recorded data. In this study, we will attempt to evaluate the efficacy of machine learning algorithms to predict stopping behavior and assess their potential for implementation alongside analytical models. A reliable, multi-method approach for estimating stopping distances and times could also enable further methods for safety in collaborative robotics such as Speed and Separation Monitoring (SSM), which monitors both human and robot positions to ensure that a safe stop is always possible. A program for testing and recording the stopping distances and times for the robot is developed. As stopping behavior varies based on the positions and speeds of the robot at the time of stopping, a variety of these criteria are tested with the robot stopping program. This data is then used to train an artificial neural network, a machine learning method that mimics the structure of human and animal brains to learn relationships between data inputs and outputs. This network is used to predict both the stopping distance and time of the robot. The network is shown to produce reasonable predictions, especially for positions and speeds that are intermediate to those used to train the network. Future improvements are suggested and a method is suggested for use of stopping distance and time quantities in robot safety applications.