Dusty Environment with Water Dripping

Mining sites often contend with a harsh combination of dust-laden air and water dripping, creating an environment that poses a significant challenge for equipment. Navigating these conditions requires specialized approaches to monitoring and maintenance.

Condition Monitoring of Remote Assets

Mining operations often involve expansive sites with assets scattered in remote locations. Monitoring the condition of equipment in these remote areas poses logistical challenges. The distance factor complicates the timely and accurate assessment of equipment health, making it crucial to overcome the complexities associated with condition monitoring in remote mining assets.

High and Low-Speed Machines

The mining industry operates a diverse array of machinery, ranging from high-speed crushers to low-speed conveyors. This diversity introduces challenges in managing the maintenance needs of machines with significantly different operating speeds. Each category demands specific attention to condition monitoring to ensure optimal performance, making it a complex task to address the varied requirements.

Robust Hardware with IP68 Rating​

Nanoprecise sets the foundation for your success with hardware built to endure the harshest conditions. Our robust hardware, featuring an IP68 rating, ensures durability and resilience in challenging environments. This commitment to robustness forms the backbone of reliable performance, addressing the demands of rigorous industrial settings.

Wireless IoT Hardware with Light Energy Harvesting

Our wireless IoT sensor, called MachineDoctor, introduces a groundbreaking solution for seamless adaptability in diverse environments. This variant is equipped with light energy harvesting capabilities, designed to function effortlessly both indoors and outdoors. By drawing power from available ambient light sources, this innovation eliminates the inconvenience of frequent battery replacements commonly faced in the field, ensuring sustained and uninterrupted functionality.

Advanced AI + Physics-Based Algorithms:

Nanoprecise integrates advanced artificial intelligence (AI) and physics-based algorithms into our solutions, marking a significant stride in predictive maintenance capabilities. This dynamic combination empowers our system to calculate instantaneous fault frequencies by correlating speed and vibration. The precision and accuracy of these insights contribute to proactive maintenance strategies, offering you a strategic advantage in optimizing operational efficiency and ensuring the longevity of your equipment.

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Case Study

In addressing the challenge of monitoring critical mining equipment in harsh conditions, a multinational mining, metals, and petroleum company partnered with Nanoprecise Sci Corp to implement a remote condition monitoring solution. The Nanoprecise team installed battery-powered wireless sensors on various equipment sets. Upon installation, these sensors immediately began sensing and transmitting data to our secure  Machine Health Analytics platform, using an encrypted network, seamlessly integrating Edge and Cloud computing. The platform then swiftly conducted data analysis with the help of advanced AI + Physics-based algorithms. 

The advanced solution detected and responded to a Stage 4 unbalance, a critical fault mode, by notifying reliability teams and facilitating immediate corrective actions. This dynamic approach to predictive maintenance not only enabled the timely identification of anomalies but also facilitated corrective measures to ensure swift resolution. 

The outcome was significant – over 12 hours of downtime saved and an impressive 20X Return on Investment (RoI). This success story highlights the transformative impact of Nanoprecise Sci Corp’s tailored solutions, showcasing enhanced reliability and substantial cost savings in mining operations. This implementation showcased the efficacy of our cutting-edge solution in enhancing operational reliability for the mining industry, particularly in navigating challenging and unpredictable working environments.