Health ensuring Reliability

Why Health must be measured?

We've explained in the previous publication that measuring and following the health of equipment is crucial for several reasons, all of which contribute to the efficiency, safety, and cost-effectiveness of terminal operations.

Regularly measuring and following the health of equipment is a strategic practice, it enables organizations to proactively manage maintenance, reduce downtime, ensure safety, optimize performance, and make informed decisions based on reliable data. This holistic approach to equipment health management ultimately contributes to the long-term success and sustainability of the organization.

Preventative Maintenance:

Monitoring equipment health allows for the identification of potential issues before they lead to failures. This proactive approach enables scheduling of preventative maintenance, reducing the likelihood of unexpected breakdowns.

Minimize Downtime:

Equipment failures can cause significant operational disruptions. By tracking equipment health, companies can minimize unplanned downtime, ensuring continuous production and service delivery.

Cost Savings:

Regular health monitoring helps in the early detection of issues, preventing major failures that can be expensive to repair. Preventative maintenance generally costs less than reactive repairs, and it extends the life of equipment, reducing capital expenditure on replacements.

Safety:

Equipment in poor health can pose safety risks to operators and other personnel. Regular monitoring and maintenance ensure that equipment operates safely, mitigating the risk of accidents and injuries.

Optimized Performance:

Healthy equipment performs more efficiently, producing higher quality outputs and consuming less energy. Monitoring health helps maintain optimal performance levels, contributing to overall productivity.

Improved Reliability:

Consistent monitoring and maintenance enhance the reliability of equipment, leading to fewer interruptions and more predictable operations. This reliability is crucial for meeting production targets and maintaining customer satisfaction.

Data-Driven Decision Making:

Health monitoring provides valuable data that can be analyzed to make informed decisions regarding maintenance schedules, equipment upgrades, and operational improvements. This data-driven approach helps in optimizing resources, spare parts and improving operational efficiency.

Compliance and Standards:

Many industries have regulatory requirements and standards regarding equipment maintenance and safety. Regular health monitoring ensures compliance with these regulations, avoiding legal penalties and enhancing reputational standing.

Asset Management:

Effective tracking of equipment health aids in better asset management. It provides insights into the lifespan and performance of assets, while helping planning for replacements and budgeting for future investments.

Enhanced Lifecycle Management:

By understanding the health and performance trends of equipment, organizations can make better decisions regarding the lifecycle management of their assets. This includes decisions about refurbishing, upgrading, or decommissioning equipment.

Sustainability:

Well-maintained equipment operates more efficiently, consumes less energy and resources, which contributes to sustainability goals. Monitoring health can help identify inefficiencies and implement measures to reduce environmental impact.

Competitive Advantage:

Companies that effectively monitor and maintain their equipment can achieve higher operational efficiency, better product quality, and lower costs, providing a competitive edge in the market.

Could health be measured as a numeric “value”?

Health is a subject and represents the health control system in charge of monitoring the healthy, error, warning, faults and interlocks; therefore cannot have a value, it needs concepts and observed properties that represent healthy.

Healthy, as a concept should reflect the equipment condition. We updated in the previous publication the CHE data model for that specific reason. The healthy concept was applied to the CHE from a global point of view. We updated the data model by applying a breakdown of not only healthy but also error/warning/fault and interlock as concepts in every sub-subject of a CHE in order to:

Know every health condition of every sub-subject
Ensure the possibility of a CHE health calculation.
Monitor and reflect the equipment's health status.

How does TIC4.0 ensure the health measurement?

Error, warning, fault and interlock are concepts to serve health monitoring. By defining those, we ensure the possibility to control and anticipate the behaviour of any signal we receive from the CHE.

Example: Tyre pressure.

We often use tyre pressure as the best example to understand the monitoring philosophy and the global process.

Below we have the behaviour of a reach stacker, more precisely, the evolution of one tyre at different moments (from t1 to t4) where we reflect the needed TIC signals along the time to be able to monitor health for this tyre (and this CHE).

In the example at below, when a warning is detected, we assume Healthy to be 'False' as per the TIC4.0 definition already created:

'Normally we assume healthy status to be OK (true) unless we sense or know we have a health problem or we might expect a health problem in the imminent future (health prediction).'

Statistic and probabilistic approach

A probabilistic approach to equipment health monitoring in the industry involves using statistical methods and probability theory to assess the likelihood of equipment failure or degradation over time. This approach leverages historical data, sensor measurements, and predictive models to estimate the probability of various failure modes and their associated risks. Here's how it can serve equipment health monitoring in the industry:

Predictive Maintenance:

By analysing historical failure data and equipment performance metrics, probabilistic models can predict when equipment is likely to fail or require maintenance. This allows maintenance activities to be scheduled proactively, minimizing downtime and preventing costly unexpected failures.

Risk Assessment:

Probabilistic models can quantify the risk of equipment failure under different operating conditions. By considering factors such as environmental conditions, operating parameters, and component interactions, these models provide insights into the likelihood and consequences of potential failures.

Condition Monitoring:

Probabilistic methods can be used to analyse sensor data from equipment in real-time, detecting early signs of degradation or abnormalities. By monitoring key parameters and comparing them to probabilistic thresholds, anomalies can be identified, and corrective actions can be taken before failures occur.

Reliability Analysis:

Probabilistic modelling allows for the assessment of equipment reliability over time. By estimating the probability distribution of failure times and failure modes, reliability engineers can identify weak points in equipment design or operation and implement improvements to enhance reliability.

Decision Support:

Probabilistic models provide decision-makers with quantitative information to support risk-based decision-making. By understanding the probability of different outcomes and their associated costs, stakeholders can make informed decisions about maintenance strategies, equipment investments, and operational priorities.

Optimization of Maintenance Strategies:

Probabilistic approaches enable the optimization of maintenance strategies by balancing the costs of preventive maintenance, corrective maintenance, and equipment downtime. By considering the probability of failure and the consequences of downtime, maintenance intervals can be adjusted to minimize overall costs while ensuring equipment reliability.

Adaptive Monitoring and Control:

Probabilistic models can adapt to changes in equipment behaviour and operating conditions over time. By continuously updating model parameters based on new data and feedback, these models can provide adaptive monitoring and control strategies that respond dynamically to evolving conditions.

The choice of probabilistic formula depends on the specific application and the type of equipment being monitored. However, several common probabilistic models and formulas are widely used in equipment health monitoring:

Weibull Distribution
Exponential Distribution
Lognormal Distribution
Bayesian Probability
Survival Analysis
Monte Carlo Simulation
Markov Chains

These are just a few examples of probabilistic models and formulas used in equipment health monitoring. The choice of the formula depends on factors such as the type of equipment, the available data, the desired level of accuracy, and the specific goals of the monitoring program. It is essential to carefully consider these factors and select the most appropriate probabilistic approach for each application.

Overall, a probabilistic approach to equipment health monitoring offers a systematic and data-driven methodology for assessing and managing the risks associated with equipment failures. By leveraging statistical methods and probability theory, we can improve reliability and empower the maintenance strategies, and minimize downtime, ultimately enhancing operational efficiency and profitability.

TIC4.0 helps by providing Health subject and associated concepts (Healthy, warning, error, fault, interlock) per equipment (or process) sub-subject.

Healthy is Boolean but every input that is coming from a sensor has a value that can be aggregated along the time and then be used as an input for the different concepts to monitor and anticipate the CHE condition.

In conclusion, the CHE health data model is made to ensure the terminal is made aware and can react as soon as a signal exists in the CHE.