Problem

A client with high-value industrial assets faced two critical business problems:

1. Sub-optimal Performance: Assets were under-producing due to inefficient, hard-to-diagnose cyclical failures.
2. Costly Manual Interventions: Failures required frequent, costly manual intervention, which in turn created significant safety and environmental compliance risks.

The legacy monitoring system (polled every 15 minutes) was blind to the real-time, high-speed events that caused these failures, leaving operators with no way to prevent them.

Process

As the Director of Engineering, I led the team tasked with building a "smart" autonomous controller to solve this.

Instead of a 15-minute poll, we retrofitted the "dumb" asset with a high-frequency sensor (an accelerometer) to listen for acoustic signatures. We then built a real-time AI agent on the device's compute module (a Raspberry Pi CM3) that ingested this new audio data.

The AI was trained to:

1. Identify the acoustic "pings" of a critical moving component.
2. Track the component's real-time speed, position, and cycle time.
3. Intelligently predict an oncoming failure state.
4. Autonomously adjust the system's control timings in real-time to prevent that failure.

Result

The new "smart" system transformed the asset from a reactive to a predictive and autonomous one. This immediately resulted in:

• 10% increase in asset production/efficiency.
• 90% reduction in costly failure events and associated compliance risks.
• The elimination of unexpected downtime for that failure mode.