APM Reliability Analysis Software

Asset reliability is the ability of an asset to perform under specific conditions throughout its lifecycle without breaking down.

To improve asset reliability, a systematic management approach is critical. The management system needs to encompass strategies to maximize asset performance and increase lifecycle through proactive maintenance, risk assessments, and condition monitoring. For visibility across all assets and predictive equipment diagnostics, reliability analysis software is a crucial tool for successful operations.

A reliability management system is a systematic approach that focuses on ensuring systems, processes, and assets consistently perform required functions under stated conditions for a defined period of time. Reliability management aims to have a specified level of reliability (performing intended function without failure or breakdown) for an operation, asset, or piece of equipment to maintain availability, reduce downtime, improve efficiency, and enhance performance.

Asset reliability software is designed to empower asset-intensive organizations to enhance reliability of physical assets. Available in different formats, such as point solutions and platforms, asset reliability software is intended to help support critical infrastructure to operate reliably, efficiently, and safely. Asset reliability software can cover a variety of equipment, turbines, generators, and more. Companies use the software to improve availability and reduce unplanned downtime. Advanced reliability software can help companies move from reactive maintenance to proactive maintenance strategies by providing operational visibility and analysis designed to work across all assets.

Asset reliability for the oil & gas industry helps ensure the availability and performance of assets while reducing the risk of downtime and maximizing safety. Asset reliability is the ability of assets, which include machinery and equipment, to perform their required functions effectively and efficiently for their intended lifecycle without failure.

Examples of key asset reliability management programs in oil and gas include:
• Data: By using advanced data solutions, predictive maintenance can help to prevent unscheduled outages and lead to significant cost savings.
• Safety: The Oil & Gas industry has some of the most dangerous jobs, therefore maintaining asset reliability is essential for safety. When asset reliability is improved with early signs of equipment failure, risk of accidents is reduced.
• Maintenance strategy: Asset reliability has various maintenance strategies including condition-based, predictive, and risk-based maintenance. These are supported by data assessment and machine learning algorithms to predict and schedule maintenance activities.
• Maintenance optimization: This program focuses on diagnostics to improve asset reliability, increase availability and output, and extend asset lifecycles while maintaining safety. Each oil & gas company will have their own comprehensive approach to managing the health and performance of their assets and their asset reliability program will leverage data-driven strategies to improve safety, efficiency, and costs.

The asset reliability process is a structured approach the energy industry uses to ensure that physical assets (e.g., turbines, transformers, substations, etc.) perform their intended functions consistently and safely over time. This process is essential for minimizing downtime, optimizing maintenance, and extending asset life.

Asset reliability is a systematic approach. The typical stages in the asset reliability process include:
1) Asset identification and criticality assessment
2) Data collection and condition monitoring
3) Reliability Analysis
4) Maintenance strategy development
5) Performance optimization
6) Continuous improvement

Asset reliability is critically important across asset-heavy industries like Power Generation, Oil & Gas, Chemicals, and Metals and Mining because these sectors rely heavily on complex, high-value equipment operating under demanding conditions.

Power Generation: Downtime can lead to blackouts, regulatory penalties, and lost revenue. The benefits of asset reliability for equipment such as turbines, boilers and generators, include ensuring consistent energy output, reducing unplanned downtime and maintenance costs, as well as supporting grid stability and regulatory compliance.

Oil & Gas: Equipment failure can cause safety hazards, environmental damage, and production loss. Benefits of asset reliability include preventing catastrophic failures and spills, improving uptime in remote or offshore operations, enhancing safety, and reducing non-productive time.

Chemicals: Unreliable assets can disrupt continuous processes and lead to hazardous incidents. Benefits of asset reliability (including for assets such as reactors, heat exchanges, and distillation columns) include maintaining product quality and process efficiency, reducing risk of chemical leaks or explosions, and supporting regulatory and environmental compliance.

Metals and Mining: Harsh environments and remote locations make maintenance difficult and costly. Key assets that need asset reliability include crushers, conveyors, mills, and haul trucks. Asset reliability maximizes throughput and ore recovery, reduces downtime, and improves worker safety and asset longevity.

Measuring asset reliability involves tracking how consistently and effectively an asset performs its intended function over time. In energy sectors such as Power Generation and Oil & Gas, this is done using a combination of quantitative metrics, failure analysis, and performance monitoring.

Key metrics to measure asset reliability include:
1. Mean Time Between Failures (MTBF)
2. Mean Time to Repair (MTTR)
3. Availability – measured by the percentage of time an asset is operational and available MTBF/MTBF + MTTR
4. Failure Rate – frequency of failures over a given time or usage
5. Overall Equipment Effectiveness (OEE) – which combines availability, performance, and quality

Asset Performance Management (APM) improves equipment availability by using data-driven strategies to predict, prevent, and optimize maintenance and operations. Here's how it works:

1. Predictive Maintenance
• How it helps: APM uses real-time sensor data, historical data, and digital twins powered by AI/ML analytics to detect early signs of equipment degradation.
• Impact: Early detection of issues by identifying patterns and anomalies, reducing unplanned downtime, and increasing availability. Predictive maintenance helps in scheduling maintenance activities during planned downtime, reducing unexpected equipment failures.

2. Condition-Based Monitoring
• How it helps: Continuously monitors equipment for health indicators such as vibration, temperature, and pressure.
• Impact: Assets are only serviced when needed, avoiding unnecessary shutdowns and extending uptime.

3. Reliability Analytics
• How it helps: APM platform and/or tools analyze historical and operational data to identify failure patterns and root causes.
• Impact: Enables smarter decisions about asset lifecycle and maintenance strategies, improving long-term availability. Identifying root causes helps in implementing long-term solutions to prevent recurring problems.

4. Optimized Maintenance Scheduling
• How it helps: APM supports Reliability-Centered Maintenance (RCM) and risk-based approaches. It also supports data-driven scheduling by maintenance activities being scheduled based on equipment condition and performance rather than fixed intervals.
• Impact: Resource allocation and efficient use of resources by focusing maintenance efforts on equipment that truly needs attention, reducing downtime and improving system-wide reliability.

5. Integration with Operations
• How it helps: APM connects with control systems, CMMS (Computerized Maintenance Management Systems), and ERP platforms. It reduces data silos and therefore risk by providing comprehensive reports and dashboards that offer insights into equipment performance and reliability.
• Impact: Streamlines workflows, ensures timely interventions, and improves coordination between maintenance and operations. Stakeholders can make informed decisions based on data-driven insights, leading to better strategic planning.

6. KPI Tracking & Continuous Improvement
• How it helps: Tracks metrics like MTBF, MTTR, and asset availability.
• Impact: Provides visibility into performance trends and supports ongoing reliability improvements.

There are several key factors that affect asset reliability across energy companies. These factors influence how consistently assets perform, how often they fail, and how long they last. Here are eight major factors and why they matter:
1. Asset design and quality: If poorly designed or substandard materials are used, this can lead to frequent failures.

2. Maintenance strategy: Proactive strategies like predictive and reliability-centered maintenance are the most effective to improve reliability by addressing failures before they occur.

3. Operating conditions: Harsh weather and environments (e.g., high temperatures, pressure, corrosive chemicals) accelerate wear and tear.

4. Human factors: Reliability depends on good training, skilled personnel, and consistent procedures/standardized workflows.

5. Monitoring & Diagnostics (M&D): Real-time monitoring using a combination of analytics, skilled personal, advanced software, artificial intelligence, and digital twins are all essential for a world-class M&D reliability program for asset-heavy industries.

6. Data and analytics: Incomplete and inaccurate data leads to poor –decision-making. Advanced APM programs and analytics enable more accurate forecasting and root cause analysis.

7. Asset age & lifecycle stage: Older assets tend to experience more failures, and new assets may have unknown failures. Both require reliability strategies that evolve with asset age.

8. Supply chain and spare parts availability: Delays in parts can extend downtime. Predictive Maintenance software can help detect issues earlier, leaving time to order spare parts.

1. Reduced downtime
• Preventive Maintenance: Regular maintenance schedules can identify and fix potential issues before they lead to breakdowns.
• Predictive Maintenance: Using AI/ML predictive analytics software to predict when maintenance is needed can prevent unexpected failures.
• Lower repair costs: Addressing issues early often means less expensive repairs compared to emergency fixes.

2. Extended asset lifespan
• Proper maintenance: Regular and timely maintenance can extend the operational life of assets.
• Reduced wear and tear: Reliable assets experience less stress and wear, leading to longer service periods.

3. Improved efficiency
• Optimal performance: Well-maintained assets operate more efficiently, consuming less energy and resources.
• Consistent output: Reliable assets provide consistent performance, reducing waste and inefficiency.

4. Lower Labor Costs
• Reduced overtime: Fewer unexpected breakdowns mean reduced need for overtime or emergency repairs.
• Skilled labor utilization: Technicians can focus on planned maintenance rather than reactive repairs.

5. Minimized replacement costs
• Avoid premature replacement: Reliable assets are less likely to need early replacement, saving on capital expenditures.
• Cost-effective upgrades: When upgrades are needed, they can be planned and budgeted for, rather than being urgent and costly.

6. Enhanced safety
• Reduced accidents: Reliable assets are less likely to cause accidents, which can be costly in terms of repairs, downtime, and potential legal issues.
• Compliance: Reliable assets are more likely to meet safety and regulatory standards, avoiding fines and penalties.

7. Better resource management
• Inventory optimization: Reliable assets mean more predictable consumption of parts and materials, allowing for better inventory management.
• Reduced waste: Efficient and reliable operations generate less waste, lowering disposal costs.

GE Vernova’s APM solutions provide an Asset Intelligence platform that includes solutions such as predictive analytics, asset health monitoring, root cause analysis, profit loss analysis, reliability analytics, and maintenance strategy optimization — all in one easy-to-use dashboard. This suite of solutions improvesvisibility and control to reduce risk and improve availability.

Industries that benefit most from asset reliability programs are those that are asset-intensive, operate in high-risk environments, and rely on continuous operations. These programs help reduce downtime, improve safety, and optimize maintenance costs.

Key industries that benefit most from asset reliability programs are:
Power Generation
• Assets like turbines, boilers, and generators must run continuously.
• Reliability ensures uninterrupted energy supply and grid stability.
• Helps avoid costly outages and regulatory penalties.

Oil & Gas
• Equipment operates in remote, hazardous environments.
• Reliability reduces non-productive time, safety incidents, and environmental risks.
• Supports upstream, midstream, and downstream operations.

Chemical Processing
• Continuous production processes are sensitive to equipment failure.
• Reliability ensures product quality, safety, and regulatory compliance.
• Prevents costly shutdowns and hazardous leaks.

Metals & Mining
• Heavy machinery and remote operations make maintenance challenging.
• Reliability improves throughput, safety, and asset longevity.
• Reduces downtime in critical operations like crushing and hauling.

Utilities (Electric, Water, Gas)
• Infrastructure like transformers, substations, and pipelines must be highly reliable.
• Reliability supports service continuity and customer satisfaction.
• Helps manage aging infrastructure and regulatory requirements.

Manufacturing
• Production lines depend on reliable machinery and automation systems.
• Reliability reduces defects, downtime, and maintenance costs.
• Supports lean operations and just-in-time manufacturing.

Transportation & Logistics
• Railways, shipping, and aviation rely on reliable vehicles and infrastructure.
• Reliability improves safety, scheduling, and operational efficiency.

Pharmaceuticals
• Equipment reliability is critical for compliance with strict quality standards.
• Prevents contamination, ensures batch consistency, and supports regulatory audits.