How Data Driven Process of Reliability and Maintenance can enhance Asset Performance

Data Driven decision

How Data Driven Process of Reliability and Maintenance can enhance Asset Performance

13 Oct. 2023
14 min read

Asset data is the foundation of any reliability and maintenance program. It provides essential information about the condition, performance, and history of the assets, as well as the potential risks and opportunities for improvement. In this blog post, we will explore how asset data can be used to optimize reliability and maintenance processes, and what tools and techniques are available to help you achieve this goal.

Data driven processes involve the sequence of steps that transform raw data into meaningful information for decision making. Such a sequence for asset reliability and performance management involves four main stages:

- Data collection: The process of acquiring data from various sources, such as sensors, databases, documents, etc.

- Data cleaning: The process of removing errors, inconsistencies, duplicates, outliers, etc., from the data.

- Data analysis: The process of applying statistical techniques or AI models to extract insights from the data.

- Data visualization: The process of presenting the data in graphical or interactive forms, such as charts, dashboards, maps, etc.

Basic Asset Data

Basic asset data includes the identification, location, specification, and configuration of the assets. This data helps to create an accurate inventory of the assets, track their movements and changes, and assign them to the appropriate owners and users. Basic asset data also enables the creation of asset hierarchies, which group assets according to their functions, relationships, and dependencies. Asset hierarchies help to organize and prioritize maintenance activities, as well as to allocate resources and costs.

Failure Mode and Failure Mechanism

Failure mode is the way an asset fails to perform its intended function. Failure mechanism is the physical, chemical, or biological process that causes the failure mode. Understanding the failure modes and mechanisms of the assets is crucial for developing effective reliability and maintenance strategies. By analyzing the failure modes and mechanisms, we can identify the root causes of failures, determine the probability and severity of failures, and implement preventive or corrective actions to reduce or eliminate failures.

Accelerated Life Tests and Work Histories

Accelerated life tests are experiments that simulate the operating conditions of the assets and measure their performance and degradation over time. Work histories are records of the past events and actions that have affected the assets, such as inspections, repairs, replacements, modifications, etc. Accelerated life tests and work histories provide valuable insights into the actual behavior and performance of the assets, as well as their remaining useful life. They also help to validate and calibrate the models and assumptions used for reliability and maintenance planning.

Statistical Techniques for Analysis

Statistical techniques are methods that use mathematical formulas and data analysis to make predictions and decisions based on data. Statistical techniques can be used to project the future reliability and availability of the assets, as well as to optimize the maintenance schedules and resources. Some of the common statistical techniques used for projection are:

- Reliability analysis: The study of the probability and frequency of failures of the assets.

- Availability analysis: The study of the proportion of time that the assets are able to perform their intended functions.

- Weibull analysis: A method that uses a specific probability distribution to model the failure patterns of the assets.

- Monte Carlo simulation: A method that uses random sampling to generate multiple scenarios and outcomes based on data.

- Optimization methods: Methods that use mathematical algorithms to find the best solutions for a given objective function and constraints

AI Models for Predicting Reliability and Availability of Assets

AI models are computer programs that use artificial intelligence techniques, such as machine learning, deep learning, natural language processing, computer vision, etc., to learn from data and perform tasks that normally require human intelligence. AI models can be used to predict the reliability and availability of assets by using data from various sources, such as sensors, images, text, etc., to detect anomalies, diagnose faults, recommend actions, etc. Some of the benefits of using AI models for predicting reliability and availability of assets are:

- They can handle large amounts of complex and diverse data.

- They can learn from new data and adapt to changing conditions.

- They can provide fast and accurate results.

- They can uncover hidden patterns and insights that may not be obvious to human experts.

Data driven process for asset reliability and performance management can help you:

- Monitor the condition and performance of your assets in real time.

- Identify trends, patterns, anomalies, risks, opportunities, etc., in your asset data.

- Communicate your findings and recommendations effectively to your stakeholders.

- Support your decision making with evidence-based information.

CMMS stands for Computerized Maintenance Management System. It is a software application that helps you manage your maintenance operations by automating tasks such as work order management, inventory management, asset management, scheduling, reporting,etc. CMMS or equivalent can be a pivotal system in data driven decisions in asset reliability and performance management by:

- Providing a centralized platform to store, access, and share your asset data.

- Integrating with other systems and devices, such as sensors, AI models, ERP, etc., to enhance your data quality and availability.

- Enabling you to plan, execute, and track your maintenance activities based on data.

- Helping you to optimize your maintenance strategies, such as preventive, predictive, condition-based, etc., based on data.

- Helping you to comply with legal and regulatory requirements, such as safety, environmental, quality, etc., by documenting and auditing your maintenance activities.

Conclusion

Asset data is a key driver of reliability and maintenance. By using the right tools and techniques, you can leverage your asset data to improve your asset performance, reduce your costs, increase your safety, and achieve your business goals. If you want to learn more about how asset data can drive reliability and maintenance, contact us today and we can help you to design and implement a data driven solution that suits your needs.