The former US Bureau of Mines funded a research project aimed at developing the enabling technology for incipient failure prediction in electric power system components as a means of reducing the injuries and fatalities that sometimes occur when equipment malfunctions. Over the ensuing years, interest in this has waxed and waned, but interest has been growing for both civilian and military applications. This paper addresses the level of turn-to-turn insulation deterioration that can be resolved using an online monitoring technique based upon an effective negative-sequence impedance detector. The detection of turn-to-turn defects is especially important because they are believed to represent the beginning stage of most motor winding failures.

This paper presents experimental and theoretical analyses used to establish electrical features that can be utilized as indicators of armature winding, field winding, or rectifier diode deterioration in brushless three-phase synchronous generators before the imperfection progresses to become a ground fault. This paper begins with the description of a 5-kVA synchronous generator specifically designed and constructed to allow for simulation of armature winding, field winding, or rectifier diode deterioration in a brushless machine. Subsequently, a description of the experimental methodology and the results of experiments are presented. Specific features that can be used as indicators of the deterioration are described, and a theoretical basis for them is provided. The proposed features can be obtained continuously during normal generator operation. As such, they can be used as a basis for both brushless and brush-fed synchronous generator health monitoring applications

The mining industry is undergoing a transformative shift driven by the rapid advancement and adoption of automation technologies. This paper provides a comprehensive overview of the current state of automation in mining, examining the technological advancements, their applications, and the prospects of automation in this critical industry. A key focus of this paper is the impact of automation on the safety and efficiency of mining operations. Highlighting the successful implementation of Automated Haul Truck Systems (AHSs) in surface mining. Additionally, this paper explores the development of automation in underground mining and its challenges, particularly limitations in communication and localization, which hinder the development and deployment of fully autonomous systems. It also provides an exploration of the challenges associated with widespread automation adoption in mining, including high initial investment costs, concerns about job displacement, and the need for specialized skills and training. Looking toward future advancements in enabling technologies will be critical for furthering automation in mining. Machine learning and AI will play an increasingly critical role in intelligent automation, enabling autonomous systems to adapt to dynamic environments, optimize processes, and make informed decisions. This paper provides a look into human–robot collaboration in the future of mining. As the industry transitions toward greater automation, it is essential to consider the evolving roles of human workers to foster a collaborative work environment. This involves prioritizing human safety, providing adequate training, and addressing concerns about job displacement to ensure a smooth transition toward a more automated future.