Asset Performance Management & PdM - Shoreline | Industrial Automation

Proactive Maintenance Strategies Minimize Production Disruption

Initial Vibration Rise: The Total Vibration Energy (TVE) rose from 0.66 ips to 0.8 ips.
Significant Vibration Increase: The TVE spiked to 1.1 ips and then to 1.18 ips.
Return to Normal Levels: Within an hour, on the same day, the TVE (total vibration energy) measured on Motor DE had dropped to 0.1 ips (which is typical for a good motor, running uncoupled, on no-load).

Supporting Data

The long-term trend below showed no gradual increase, i.e. the trend indicates slow and persistent development of a machinery fault, and was persistent for several months.

Solution:

Shoreline’s team conducted a thorough post-event analysis utilizing vibration data and trend charts. Key findings included:

No Gradual Degradation: Long-term vibration trends did not show a slow and persistent increase, suggesting a rapid failure mechanism.
Axial Vibration: Predominant vibration signals were in the axial direction, pointing towards misalignment or looseness in the belts, sheaves, or foundation bolts.
Non-harmonic Peaks: Frequency analysis revealed non-harmonic peaks indicative of belt/pulley issues and potential structural resonance.

Evidence of Success:

Based on the analysis, Shoreline provided actionable recommendations:

Realignment: Realign belts and sheaves to ensure proper operation.
Looseness Check: Inspect and tighten belts, sheaves, and foundation bolts to eliminate excessive vibration.
Structural Assessment: Evaluate the motor/fan mounting structure for looseness and potential resonance issues.

Results:

Root Cause Identification: Successfully identified the root cause of the belt failure as misalignment/looseness.
Reduced Downtime: Facilitated quicker repairs and minimized production disruption.
Improved Predictive Capabilities: Enhanced anomaly detection algorithms to prevent future occurrences and improve overall asset reliability.

Shoreline's actions to Improvement Future Anomaly Detection:

Faster Trend Analysis: Reduced averaging time for vibration values to detect rapid changes more effectively.
New Anomaly Detector: Introduced an algorithm specifically designed to identify failures like the belt failure, where the motor continues running unloaded after the event.

Conclusion:

This case study demonstrates the value of Shoreline’s Asset Performance Management & Predictive Maintenance services in minimizing downtime and optimizing asset performance. By combining expert analysis with advanced predictive technologies, Shoreline provides clients with the insights and tools necessary to maintain critical equipment and ensure operational continuity.

About Shoreline AI

Shoreline AI’s plug-and-play asset performance management delivers breakthrough simplicity and cost efficiencies. Completely self-installed by non-experts, smart sensors automatically connect to the cloud and are auto-provisioned via a rich library of 30,000+ pre-built asset physics models.

This cloud-native approach requires no new CapEx, on-site experts or data scientists, operationalizing in days and delivering powerful machine-specific analytics. This highly secure, 100% subscription approach creates unprecedented industrial APM economics and scales easily for new applications such as emissions monitoring.

Shoreline AI helps clients in asset-intensive industries maximize the performance and profitability of their operations, create a proactive and predictive approach to asset management, and accelerate sustainability initiatives. The company’s solutions are designed for machinery serving the energy, manufacturing, pharma and data-center cooling industries.