Unleashing the Power of Groq: A Revolution in AI Performance
Unsupervised learning, a cornerstone of artificial intelligence, has emerged as a powerful technique that enables machines to uncover patterns and insights from unlabelled data.
Artificial Intelligence (AI) and predictive technologies are improving mining safety and employee protection by providing new real-time insights to avoid dangerous situations and establish alerts to immediately mitigate risky conditions.
The hazardous nature of mining operation and working conditions, remote locations and sites as well as the high cost and large machinery used would lead us to think mining would lead the artificial intelligence and machine learning revolution. However, the opposite is the case. It’s only been in recent years that mining is adopting AI and ML into its operation driven by volatile commodity prices, wireless technology advances, and increasingly higher costs. Mining and mineral processing adoption of these technologies is driven to improve operational efficiency, increase yield, and improve employee and operational safety.
According to the Occupational Safety and Health Administration (OSHA), more than 20 percent of accidents in the industry occur during unplanned downtime or machine failures. According to the same survey, out of the 80% of accidents that happen during normal routine maintenance, many occur due to a lack of expertise in the subject matter, defective work methods, and lack of preliminary diagnostic before trying to fix the machine.
Use Cases: AI Improves Mining Safety
Machine Vision to Monitor Proper Use of PPE and Harness
AI-enabled camera can be used to detect and monitor the proper wearing of personal protective equipment. The device can also monitor employees working at a certain height that needs wearing of a harness. Not only can the camera determine the proper use of PPE and harness but also identify if the PPE is properly tethered.
Other use of machine vision in mining operation includes tracking interactions between workers and machinery, monitoring the status of machine guarding, and checking or workers outside of designated areas.
With machine vision, there is round-the-clock monitoring of operation and worker safety reducing the frequency and reliance on one person doing the round once every shift or a couple of hours a day.
Heat Mapping and Fatigue Monitoring
Cameras, IoT sensors, and wearables can generate heat maps to identify and show high-risk activities in the mine site. Combining this with a multi-layer perspective on risks such as dynamic employee risk profile, identification of high-risk operations, staff working long hours, and tasks requiring high concentration activity, management will have a better understanding of the risk intensity. With an established baseline for each operation, risk can be reduced by reassigning workers who have had long hours to less risky tasks in the meantime or temporarily suspending current tasks to prevent incidents.
Deploying AI and ML into the mining operation to improve occupational health and safety (OH&S), increase operational efficiency and increase yield require professional assistance and collaborative effort. Consultants must work closely with mining professionals to understand operational requirements and the most effective use cases. It is also important for employees to understand that AI technology is adopted to improve efficiency and give them opportunities to work on more productive tasks and not to replace them.
