An Industrial Revolution

Autonomous real-time sensor algorithms delivering insight where it happens

Quality, Safety and Compliance Monitoring

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.

The CHALLENGE:

Access to operational data needed to drive effective compliance programs is challenging to capture, to effectively use, and is often hampered by commercial pressures. Furthermore, demonstrating value and business benefits often requires significant and complex pilot projects. Just getting meaningful edge data on the front-end to drive analytics and reporting can be a monumental task itself.

Where SensiML Can Help

Application Summary

SensiML Analytics Toolkit combined with modern smart sensor IIoT endpoints can enable true insights to be quickly tailored from available physical sensors like worker wearable devices, intelligent machine sensors, and process control inputs where they occur. With edge algorithms, the potential deluge of ensuing data can be effectively managed and only insights of value conveyed for downstream reporting and analysis.

  • Real-time telemetry data driving compliance policy
  • Reduction of redundant data to reveal only true insight
  • Learning models that tailor to individual workers and operators

Production Manufacturing and Predictive Maintenance

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.

The CHALLENGE:

Across nearly every sector, organizations are challenged with maximizing operational uptime and production yields while not skipping a beat. Unscheduled downtime means lost revenue, lost margins, and an idled workforce. Poor product yields translate directly to the bottom line with reduced margins, higher operating overhead, and uncompetitive product costs. Anticipating these adverse production events requires insight on complex processes and machinery.

Where SensiML Can Help

Application Summary

SensiML’s Analytics Toolkit can transform complex high-frequency sensor data from many points in the process to derive production insight in real-time. With ML teachable algorithms, SensiML enabled IIoT sensors can Identify systems anomalies associated with pending equipment failures and yield losses.

  • Reduce maintenance costs through IoT systems monitoring
  • Optimize operations through improved systems reliability
  • Increase production predictability using machine learning to forecast maintenance needs
  • Maximize profitability by improving systems uptime and yield

Structural Health Monitoring

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.

The CHALLENGE:

Infinitesimal cracking is one of the most dangerous types of material failure and found where components are put under continuous or cyclical stress. These types of cracks often go unnoticed or detected only with infrequent testing. Failure to identify stress-related component fatigue poses significant safety and financial risks.

Where SensiML Can Help

Application Summary

SensiML Analytics Toolkit combined with high-bandwidth acoustic emission sensors can provide the pattern recognition and edge sensor processing needed to catch fleeting critical events that portend risk for catastrophic failure. Smart structural sensors can replace expensive and infrequent manual inspections. SensiML AI sensor processing integrated directly into the sensor modules offers real-time responsiveness, network independence, and ubiquitous deployment to greatly improve on existing methods.

  • Identify potential risks before they impact health and safety or operations
  • Comply with regulatory requirements for safe operations
  • Reduce the time and expense associated with managing predictive structural health monitoring

Use Cases

Consumer

Are You A Maker Or Inspired AI Innovator?

Visit the SensiML Data Depot repository and review available application examples, documentation, and sample datasets. Each application includes summary information on the hardware and sensor used, number of sample captures, and sector.