An Industrial Revolution

Compliance monitoring helps your company detect and prevent policy violations protecting your organization from fines and lawsuits. Quality measures ensure continuous process improvement and are often demanded by customers enforcing industry quality compliance for approved vendors. Effective quality, safety, and compliance management depends on collecting actionable insight and operational data when and as it occurs. Such telemetry data is the basis for downstream analysis and reporting used to achieve corporate goals, customer quality, and regulatory / industry compliance standards. Scalable collection and processing of this telemetry data rests on automation and technology solutions that can make these processes efficient without overtaxing core operations.

Monitoring for suboptimal production processes and future equipment faults/failure allows manufacturers to schedule maintenance intelligently. Often equipment degradation can be caught early and scheduled before lines-down failures occur. Early warning depends on the analysis of process or mechanical anomalies detectable by sensitive IIoT edge sensors. Continuously monitoring systems for future failures allows manufacturing organizations to plan maintenance during normal maintenance cycles. While significantly reducing the costs and downtime associated with unplanned maintenance, predictive maintenance also reduces unnecessary manual check-ups and the premature replacement of equipment before the end of normal duty cycles.

Whether identifying microscopic cracks and material fatigue in bridges, buildings, pressure vessels, airframes, or many other applications, structural health monitoring can provide a valuable early-warning system by detecting the minute signals emitted from materials undergoing stress and deformation at a microscopic level. By using acoustic emission sensors capable of measuring vibration and ultrasonic energy at extremely high frequency, these early microscopic material faults can be detected in advance of macroscopic component failure. Until recently, the associated sensors, signal conditioning and amplification equipment and spectrum analyzer test equipment involved, made this technology inaccessible for all but the most critical, high value equipment and infrastructure. Now with advances in sensor technology, edge processing and edge AI tool like SensiML, these methods are finding their way into a much broader array of applications.