Rotating Equipment Condition Monitoring Maintenance & Fault Prediction using Wireless Sensors

Problem

An industrial conglomerate faced challenges with their existing maintenance practices for rotating equipment, particularly screw compressors and their motors. Despite regular preventive maintenance processes and periodic vibration analyses, the company experienced a year-over-year increase in equipment failures. The delays in procuring components for rebuilding further complicated the situation. The traditional inspection regime did not yield the intended results, and the company needed a more effective and proactive approach to prevent failures and optimize maintenance.

Approach

To address the challenges and enhance equipment maintenance, the company adopted a Condition Monitoring Maintenance (CMM) application with wireless sensors and AI-based analytics. The following approach was implemented:

• Smart Wireless Sensors: Proprietary wireless sensors were installed on multiple refrigeration chillers, each comprising a 500HP AC motor driving an 800-ton Frick ammonia twin screw compressor. These sensors continuously monitor tri-axis vibration and temperature data from the rotating equipment. 
• AI + Physics-based Analytics: The CMM application utilizes AI and physics-based analytics models developed by Algo8 to analyze the vibration and temperature data in real-time. The AI model learns from the collected data to create an evolving condition map of the equipment across its operating range. 
• Early Fault Detection: By continuously analyzing small snippets of vibration and temperature information, the application detects anomalies and deviations from normal equipment behavior. It can predict potential failure modes and provides actionable insights for timely corrective actions. 
• Real-time Alerts: The CMM application immediately alerts the maintenance team through the app and email when it identifies increasing overall vibration levels and an increasing noise floor, indicating potential issues with the equipment. 

Benefit

The implementation of the CMM application with wireless sensors and AI-based analytics yielded significant benefits: 

• Cost Savings: The early detection of potential failures and condition-based maintenance allowed the company to save 90% of direct costs associated with rebuilding, including rigging and labor expenses. 
• Avoided Downtime and Production Loss: By identifying issues early, the application helped the company avoid unplanned downtime of 3 hours and prevented production losses during the same period. 

Impact

The adoption of the CMM application resulted in notable financial benefits for the industrial conglomerate:

• Direct Cost Savings: The early detection of equipment issues and consequent cost savings amounted to $40,000, providing a greater than 10x return on investment (ROI) for the motor-compressor. 
• Avoided Production Losses: Additionally, the application’s proactive approach prevented 3 hours of line downtime and production loss, resulting in further undisclosed dollar savings. 

By leveraging smart wireless sensors and AI-based analytics for rotating equipment condition monitoring, the company achieved cost savings, reduced downtime, and improved maintenance efficiency, ultimately leading to enhanced operational reliability and profitability. 

Predictive Maintenance for Rotating Equipment (Compressor – MAB)

Problem

The main air blower (MAB) in the Fluid Catalytic Cracking (FCC) unit of a refinery is a critical centrifugal compressor that provides air necessary for combustion in the regenerator. The smooth functioning of the MAB is essential for the overall performance of the FCC unit. Unplanned shutdowns of the MAB can result in significant losses, including direct production losses, additional maintenance costs, and indirect efficiency losses due to unwanted downtime. The challenges faced by the refinery include:

• High Downtime Impact: Unplanned shutdown of the MAB leads to the shutdown of the entire FCC unit, resulting in substantial losses in terms of production, maintenance, and efficiency. 
• Frequency of Unplanned Shutdowns: The MAB has experienced multiple unplanned shutdowns, leading to financial losses and decreased production efficiency. 
• Monitoring Complexity: The MAB requires monitoring of over 200+ tags, and the lack of a single source of truth for 20 assets causes incomplete visibility into the system. 

Approach

To minimize unplanned shutdowns and gain visibility over MAB operations, the refinery adopted Algo8’s Predictive Maintenance application. The approach includes the following steps:

• Process and Machine Data Integration: The Predictive Maintenance application leverages both process and machine data to detect issues early. It uses historical data from the equipment and the process to train and fine-tune anomaly and predictive models. 
• Anomaly Detection Engine: Algo8’s Anomaly Detection Engine uses supervised and unsupervised machine learning algorithms to identify anomalous behavior in the MAB. It also determines the contributing factors responsible for the anomalous behavior, which could be related to process or operational issues. 
• Root Cause Analysis with Generative AI: The application uses Generative AI enabled Failure Mode and Effects Analysis (FMEA) to identify the failure mode responsible for the primary cause of the problem, leading to a better understanding of the underlying issues. 
• Predictive Model: The Predictive Maintenance application includes a predictive model that forecasts the remaining time to failure of the equipment. This early prediction allows operators to take action and prevent unplanned shutdowns. 
• Recommendations with Generative AI: The application provides recommendations through Generative AI enabled what-if scenarios. These recommendations help manage equipment failures proactively and extend the operating window of the MAB. 

Benefit

The implementation of Algo8’s Predictive Maintenance application resulted in several benefits for the refinery:

• Minimized Production Losses: By avoiding unplanned shutdowns, the refinery experienced minimized production losses, ensuring continuous operation and optimized productivity. 
• Reduced O&M Spend: The reduced frequency of unscheduled maintenance downtime led to cost savings in operations and maintenance expenses. 
• Decreased Safety Incidents: Predictive maintenance reduced the likelihood of safety incidents caused by unexpected shutdowns and equipment failures. 

Impact

The successful implementation of Algo8’s Predictive Maintenance application had a significant impact on the refinery: 

The refinery managed to save $7.5 million per shutdown, ensuring 100% asset availability by proactively addressing issues and preventing unplanned shutdowns. This not only improved the financial performance of the refinery but also increased overall operational efficiency and safety. 

Real-Time Calculation of Asset Health based on Corroded Area Percentage

Problem

Asset management in industries like manufacturing, oil and gas, and infrastructure often involves monitoring and inspecting assets for signs of corrosion, which can lead to significant safety risks and operational inefficiencies if left unaddressed. Traditional manual inspection methods are time-consuming, costly, and may not provide real-time insights into the health of assets. Identifying areas with high corrosion rates is crucial to prioritize maintenance activities and prevent potential failures. The challenge is to develop a system that can accurately and efficiently calculate the health of assets in real-time based on the percentage of corroded area, reducing the need for manual inspections and enabling prioritized maintenance planning.

Approach

The approach to implementing real-time asset health calculation based on the percentage of corroded area involves leveraging IoT sensors, computer vision, and data analytics. Here’s a breakdown of the key steps:

• IoT Sensor Deployment: Install IoT sensors on assets, both internally and externally, to collect data on corrosion levels. These sensors can measure environmental factors like temperature, humidity, and corrosive gases, as well as asset-specific parameters like wall thickness or coating condition. 
• Data Acquisition and Preprocessing: The IoT sensors continuously gather data on corrosion-related parameters. This data is preprocessed to remove noise, handle missing values, and ensure its accuracy and consistency. 
• Computer Vision for Corrosion Detection: For assets with visual surfaces, deploy computer vision algorithms to analyze images and detect areas of corrosion. These algorithms can segment corroded regions and calculate the percentage of the corroded area on the asset surface. 
• Data Integration: Combine data from IoT sensors and computer vision outputs to create a comprehensive view of asset health. The integrated data should include the percentage of corroded area, environmental conditions, and other relevant factors. 
• Health Index Calculation: Develop a health index or score based on the percentage of corroded area and other relevant parameters. The health index should represent the asset’s overall condition and potential risk of failure. 
• Real-Time Data Processing: Utilize data analytics and machine learning algorithms to process the integrated data in real-time. This ensures that the health index is continuously updated as new data is collected, providing real-time insights into asset health. 
• Prioritization of Maintenance Activities: Based on the health index, prioritize maintenance activities and intervention strategies. Assets with higher corrosion levels or deteriorating health would receive higher priority for maintenance, allowing for proactive planning and resource allocation. 
• Alerts and Notifications: Implement an alerting system that notifies maintenance teams when an asset’s health index reaches a critical threshold or requires immediate attention. This enables timely responses and reduces the risk of unexpected failures.

Benefit

• Reduced Manual Inspection: By automating the corrosion detection process using computer vision and IoT sensors, manual inspection requirements can be reduced by up to 90%, saving time and labor costs. 
• Real-Time Asset Health Monitoring: The system provides real-time updates on asset health, allowing proactive maintenance planning and minimizing the risk of unplanned downtime or failures. 
• Cost Savings: Prioritizing maintenance activities based on asset health helps allocate resources more efficiently, reducing unnecessary maintenance costs and optimizing asset performance. 
• Enhanced Safety and Reliability: Timely identification of corroded areas allows for prompt maintenance and repair, enhancing safety and prolonging asset life. 
• Improved Asset Management: Accurate and continuous monitoring of asset health enables better asset management decisions, leading to increased operational efficiency and reduced operational risks. 
• Increased Sustainability: Proactive maintenance and optimized resource allocation contribute to a more sustainable approach to asset management by reducing waste and unnecessary interventions. 
• Compliance and Regulatory Requirements: Efficient asset health monitoring aids in meeting regulatory requirements related to asset inspection and maintenance.

By implementing a real-time calculation of asset health based on the percentage of corroded area, organizations can streamline maintenance operations, enhance asset reliability, and reduce inspection costs, leading to improved overall asset management and operational performance.