Globally, there are more than 300 million industrial electrical motors running. These motors consume around 70% of the power delivered to industrial facilities. They serve as the backbone of the industrial world, running 24/7 to support production lines. However, these motors experience faults which if left unaddressed, can bring a whole production line down. When this happens, the company can lose upwards of 260,000 USD per hour causing unproductive workforce, spoiled raw materials, and unsatisfied customers. Moreover, the electrical efficiency of the motor decreases over time as it experiences wear and tear, increasing energy consumption.
OneWatt developed an AI-based predictive maintenance system that listens and understands noise coming out of electrical motors. This system utilizes sensors called EARS that is totally non-invasive and non-contact, together with DSP and machine-learning it detects and predicts faults before they happen, avoiding unplanned downtime. The system also ensures that motors are working in its most efficient, decreasing energy consumption, and improving the productivity of maintenance.
The first goal is to analyze the commercial feasibility of the technology and identify which market segments, requirements, and strategies to be used. Second, identify parameters and methodologies to minimize energy consumption, increase asset lifespan, and increase reliability of predictions.