Semiotic Labs develops a sensor for predictive analytics on rotating machinery like wind turbines and motors. The Netherlands-based company has raised over $11 million, closing a $7.8 million Series B in 2019, and has partnered with Schneider Electric to integrate its product with Schneider's asset management service.

The company's device enables predictive maintenance, identifying over 90% of failures at up to 5 months in advance by analyzing electric waveforms. For wind turbine applications, the company claims this can result in a 175% improvement in ROI. The device can also provide energy efficiency recommendations by identifying suboptimal operation.

Semiotic Labs offers the SAM4, a current and voltage sensor that installs directly in the motor control cabinet. The sensor collects data which is fed into an AI-driven analytics platform to identify faults. Asset performance can be monitored through a dashboard, showing real-time data and historical performance. The platform also passes on alerts when new faults are identified, classifying them based on their severity and immediacy.

SaaS, per unit

• Motor control cabinet installation sensor is installed alongside motor controls, providing quick and easy deployments for assets that are in difficult to reach areas
• Electric waveform analysis analyzes motor current and voltage signatures to identify more faults than standard vibrational analysis

• Predictive Maintenance identify a range of faults in rotational equipment including stator, coupling, bearing, and hydraulic failure
• Energy efficiency identify problems that are resulting in abnormally high energy consumption for rotational equipment or pinpoint underperforming motors for replacement
• Asset management monitor status and alerts of assets across a site, viewing single-asset or portfolio dashboards