Situation

Usage of smart sensors to monitor motors, mid-sized gearboxes and fans data.The machine learning (ML) model uses vibration and standard signals in order to combine with process information to anticipate potential failures in the equipment. ML model also makes a recommendation on how to operate the equipment to increases up-time, lifetime and minimizes unplanned downtime.

Challenge

Manufacturing companies face challenges in identifying potential equipment failures and taking proactive maintenance actions. The reliance on manual inspections and periodic maintenance checks limit their ability to detect early signs of degradation or impending failures. As a result, unplanned equipment breakdowns lead to costly repairs, extended downtime, and reduced manufacturing capacity.

Solution

  • Online sensors were strategically installed on critical equipment, including turbines, generators, pumps, and bearings. These sensors continuously monitored various parameters such as temperature, vibration, lubrication, and electrical current.
  • Sensors data was collected in real-time and transmitted wirelessly to a centralized data acquisition system. This system securely stored and processed the data in a cloud-based environment.
  • Advanced analytics techniques, including machine learning algorithms, were applied to analyze the data to detect anomalies, patterns, and early indicators of equipment degradation or fail
  • Based on the analysis results, the system generated alerts and notifications to the maintenance team. These alerts were sent via email, SMS, or through a dedicated dashboard, enabling prompt attention to potential issues.
  • The maintenance team received actionable insights from the system, facilitating proactive maintenance planning. Maintenance schedules were optimized based on equipment condition and criticality, ensuring timely repairs and minimizing unplanned downtime.
  • The system provided real-time performance tracking and reporting capabilities. Key performance indicators (KPIs) such as equipment health, efficiency, and power generation capacity were monitored and reported to management for better decision-making.

How smart sensors plays a crucial role in power energy generation industry to ensure reliable and efficient operations.

Results

Smart sensors plays a crucial role in power energy generation industry to ensure reliable and efficient operations.

  • Improved Equipment Reliability: Early detection of equipment anomalies and impending failures allowed for proactive maintenance actions. This resulted in reduced equipment downtime, extended equipment lifespan, and improved overall reliability.
  • Optimized Maintenance Practices: Proactive maintenance planning based on machine condition data enabled the Power Generation company to optimize maintenance efforts. This led to reduced maintenance costs, minimized equipment failures, and improved maintenance resource allocation.
  • Enhanced Power Generation Capacity: By minimizing unplanned equipment downtime, the power generation capacity of the plant was optimized. This resulted in increased electricity production and improved revenue generation.
  • Cost Savings: Proactive maintenance actions and reduced equipment failures led to cost savings associated with emergency repairs, replacement parts, and production losses due to downtime.
  • Improved Safety: Early detection of equipment anomalies allowed for timely corrective actions, reducing safety risks associated with equipment failures. This contributed to a safer work environment for employees and reduced the potential for accidents.
  • Data-Driven Decision Making: The online monitoring system provided real-time insights and performance tracking, enabling data-driven decision making. This helped management optimize operations, plan equipment upgrades, and improve overall plant efficiency.

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ML model also makes a recommendation on how to operate the equipment to increases up-time, lifetime and minimizes unplanned downtime.

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