Predictive Maintenance

What is predictive maintenance?

Predictive maintenance is a maintenance strategy that uses a combination of data and analytics techniques to predict when an in-service machine requires service.

To implement a predictive maintenance strategy, investments in hardware and software to capture and persist data are required. The organization needs to maintain a quality stream of operational data, typically from sensors installed on the machine. Other sources of data, such as maintenance records, failure events, inspection records, and engineering diagrams, among others, enable a more complete predictive maintenance strategy with holistic monitoring. Analytical or machine learning techniques are then applied on top of the data to create predictive models.

Why is predictive maintenance important?

Predictive maintenance is an important operating strategy that helps organizations reduce risk, control costs, and improve productivity. Predictive maintenance is especially important for assets that have long replacement lead time, costly service interruptions, and high safety or productivity risks associated with over-maintenance.

With a predictive maintenance strategy, organizations can:

  • Reduce downtime by predicting when equipment is likely to fail.
  • Minimize unplanned interruptions to operations.
  • Control costs by reducing unnecessary repairs and optimize availability of part replacements.
  • Improve productivity by enabling teams to focus on critical risks and reducing interruptions.
  • Improve productivity by guiding teams to prescriptive failure modes versus conducting lengthy root cause investigations.
  • Avoid unnecessary outages associated with over-maintaining machines on time-based schedules.
  • Enhance safety by preventing emergency repairs and catastrophic events.

Why use AI for predictive maintenance?

AI-powered predictive maintenance software brings together operational data and advanced machine learning techniques to help organizations accurately predict the risk of asset failures. Rooted in the concepts of condition-based maintenance, AI-powered predictive maintenance goes a step further by using AI to uncover patterns that cannot be easily uncovered with traditional condition-monitoring techniques and feedback loop to improve over time.

How Does C3 AI Enable AI-Powered Predictive Maintenance?

C3 AI provides a pre-built AI-powered predictive maintenance application, C3 AI Reliability, to help organizations implement a predictive maintenance strategy. The application unifies operational data from multiple sources, such as sensors, asset templates, maintenance records, and operating manuals, and applies advanced machine learning and generative AI techniques to identify equipment risks in advance and providing recommend actions to prevent unplanned downtime and emergency repairs.

Examples of customers using C3 AI Reliability to implement AI-powered predictive maintenance include:

  • Oil & Gas: Shell deploys C3 AI Reliability application to monitor more than 14,000 critical pieces of equipment across its global operations.
  • Aerospace & Defense: The US Department of Defense deploys C3 AI Reliability application to predict subsystem failures in multiple aircraft platforms.
  • Industrial Manufacturing: Georgia-Pacific, subsidiary of Koch Industries deploys C3 AI Reliability application to predict failure of manufacturing equipment.

C3 AI Reliability is used to monitor tens of thousands of assets in operation globally across these customers and many more.

The application is built on the C3 AI Platform, an end-to-end platform for developing enterprise AI applications. The C3 AI Platform provides a scalable and secure approach to enterprise AI. It provides the tools and capabilities required to rapidly design, develop, and operate advanced, industrial-scale enterprise AI applications.

The unique model-driven architecture of the C3 AI Platform allows organizations to build AI applications with less code, time, and effort than other approaches. It includes an end-to-end architecture to integrate with existing enterprise software systems, ingest into domain object models, apply out-of-the-box and configurable AI algorithms, and enable end user interaction in intuitive, simple user interface applications. C3 AI also provides a proven methodology and best practices for customer developers to configure, extend, and develop proprietary AI applications. C3 AI shares this methodology in a center of excellence collaboration model, delivering proven results as demonstrated through a decade of experience in working with some of the world’s largest organizations on industrial-scale use cases.

Examples of Enterprise-Scale Predictive Maintenance

Enterprise AI
at Georgia-Pacific

Working with C3 AI, Georgia-Pacific (GP) has emerged as an industry leader in the adoption of AI systems in the manufacturing industry.

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Enterprise AI
at Shell

Shell has a rich history with digital technology and is committed to driving transformation at scale using enterprise AI.

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Predictive Maintenance Use Cases

AI-Driven conditional maintenance finds applications across various industries and sectors:

  • Manufacturing: Predictive maintenance is widely used in manufacturing plants to monitor the condition of machinery such as CNC machines, motors, and conveyor systems. By detecting anomalies in real-time data, manufacturers can avoid costly breakdowns and optimize production schedules.
  • Energy and Utilities: The energy sector employs predictive maintenance to ensure the reliability of power generation equipment, such as turbines and transformers. This minimizes downtime and maximizes energy output.
  • Transportation: In the transportation industry, conditional maintenance is used to monitor the condition of vehicles, aircraft, and railway systems. This not only improves safety but also reduces operational disruptions.
  • Healthcare: Hospitals utilize PdM maintenance to monitor critical medical equipment, such as MRI machines and ventilators. Ensuring the availability of these devices is essential for patient care.
  • Oil and Gas: In the oil and gas industry, predictive maintenance is crucial for maintaining drilling equipment, pipelines, and refineries. By predicting equipment failures, companies can prevent oil spills and other environmental disasters.
  • Retail: Retailers use predictive maintenance to keep refrigeration units and HVAC systems in working order, ensuring the quality of perishable goods and a comfortable shopping environment.

Generative AI based maintenance assistant: Maintenance personnel must navigate lengthy manuals, word-of-mouth training from experienced peers, diverse data sets, and software systems to find answers to their asset maintenance questions. Generative AI driven enterprise search can accelerate time-to-insight and improve productivity and effectiveness of maintenance personnel.



Challenges in Implementing Predictive Maintenance

While the benefits of predictive maintenance are clear, implementing AI-Driven Predictive Maintenance comes with its own set of challenges:

  • Data Quality: High-quality data is essential for accurate predictions. Poor data quality or incomplete data can lead to inaccurate predictions and unreliable maintenance schedules.
  • Data Integration: Organizations often have data stored in disparate systems. Integrating data sources and ensuring data flows smoothly is a complex task.
  • IoT Sensor Deployment: Installing sensors on equipment can be costly and time-consuming. Deciding which sensors to use and where to place them requires careful planning.
  • Algorithm Selection: Choosing the right machine learning algorithms and models is critical for accurate predictions. This requires expertise in data science and AI.
  • Maintenance Culture: Shifting from a reactive maintenance culture to a proactive one can be challenging. Maintenance teams may resist change or lack the necessary skills to implement predictive maintenance.
  • Cost Considerations: While predictive maintenance can save costs in the long run, the initial investment in technology and training can be substantial.

Key Considerations in Implementing Predictive Maintenance

If you are initiating the implementation of predictive maintenance, there are many considerations, including:

  • Scale – Many AI implementations do well as proof-of-concepts but falter as they are scaled into production. Ensure that the design of the AI models and application have an architecture and components that will scale across your enterprise.
  • Speed – Many AI applications look deceptively easy to implement. But data integration, AI model development and testing, and developing application workflows that fit in with business processes, can take much longer than anticipated.
  • Value – Selecting high-value use cases that will provide substantive and measurable returns is critical in making a case for an AI application, especially in the early days of digital transformation.
  • Maintenance Culture: Foster a culture of proactive maintenance by training and educating maintenance staff about the benefits of predictive maintenance. Incentivize collaboration between maintenance and operations teams.
  • Security – there have been many instances when enterprise data and IP have been inadvertently leaked. AI governance is essential in ensuring that sensitive operational data is always secure and confidential.

AI Applications for Predictive Maintenance

C3 AI Reliability provides reliability engineers, maintenance managers, and executives with comprehensive monitoring across critical and non-critical assets and enables proactive, AI-based predictive maintenance. Learn More.

C3 AI Reliability

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