Harnessing the Power of Predictive Maintenance in Automation Systems

Introduction

An automation system combines intelligent software and hardware, i.e., sensors, actuators, and controllers, designed to perform a specific function without or with minimal human intervention. In the context of industrial and manufacturing processes, automation systems greatly enhance operational efficiency and precision, significantly improving productivity and product quality. However, when a component of an automation system fails unexpectedly, it can disrupt an entire production or assembly line and bring about major financial losses.

In such a case, if you can identify the possibility of the component failing beforehand, you can carry out appropriate preventive measures to avoid unexpected breakdowns. This is possible with predictive maintenance.

What’s Predictive Maintenance?

Predictive Maintenance is a proactive, data-driven maintenance strategy that involves monitoring the performance and condition of different equipment or systems in real-time to identify signs of potential failures before they occur. This approach leverages various technologies, including Artificial Intelligence, Internet of Things, Machine Learning, Advanced Data Analytics, and Sensor Technology, to track performance trends, detect anomalies, and identify potential equipment or system failures well in advance, allowing maintenance teams to schedule and execute maintenance activities promptly.

As a data-driven maintenance approach, predictive maintenance seeks to forecast equipment failures based on real-time and historical data analysis. This predictive capability enables manufacturers to avoid unexpected equipment breakdowns and circumvent costly production disruptions by addressing technical issues promptly before they escalate into major problems. This often results in improved productivity and significant cost savings.

Essentially, the primary objective of predictive maintenance is to maximize equipment uptime, improve operational efficiency, reduce maintenance expenses, and extend the overall service life of an organization’s technical assets. It also allows manufacturers to plan maintenance operations around their normal production schedules, reducing production downtimes and optimizing resource allocation.

How Does Predictive Maintenance Work?

As previously mentioned, Predictive Maintenance works by leveraging advanced automation technologies such as the Internet of Things (IoT), sensor technology, Machine Learning, and data analytics to monitor an asset’s performance and health status and predict potential technical failures. Here is a step-by-step overview of how this type of maintenance works:

Data Collection

The fundamental step in executing a successful predictive maintenance scheme in automation systems is data collection. IoT-enabled sensors are strategically installed on various components of critical automation equipment and machinery to continuously monitor a wide range of parameters such as vibration levels, operating temperature, system pressure, electric currents and voltages, relative humidity, acoustic emissions, and more. In doing so, these sensors capture real-time data of the parameters they monitor and transmit it to a centralized computing system for analysis.

Data Analysis

In this step, AI and machine learning algorithms process the collected sensor data and analyze available historical data (i.e., past equipment failures and performance data) to track performance trends, detect anomalies, and identify failure patterns. As such, the real-time sensor data is transformed into actionable insights that help forecast when the equipment/system is likely to fail and proactively schedule maintenance. In addition, the historical context also provides useful insights into how the equipment/system will behave over time.

Predictive Models

Data scientists then create predictive models, and through various Machine Learning techniques, the models are continually trained using real-time sensor readings and historical data to predict failure periods of each technical asset being monitored and forecast when maintenance will be required. With time, these Machine Learning models can be fine-tuned to increase their predictive capabilities and improve the accuracy of their predictions until unplanned downtimes can be avoided entirely. 

Maintenance Alerts

Upon predicting when equipment might fail or require maintenance, the predictive models generate alerts sent to maintenance teams, enabling them to schedule and execute maintenance activities proactively. By carrying out maintenance tasks at the right or optimal time, organizations can reduce downtimes, minimize maintenance expenses, and improve an automation system’s overall performance and reliability.

Use Cases of Predictive Maintenance in Automation Systems

Condition Monitoring

Generally, condition-based monitoring is an important element of predictive maintenance. It involves using IoT-connected sensors and AI-powered data analytics to continuously assess the health status of an equipment/system, alerting maintenance teams whenever deterioration or anomalies that may lead to equipment/system failure are detected or when predefined reliability thresholds are exceeded. Sudden drops in the equipment’s health status index could indicate impending failure.

Here’s how predictive maintenance is implemented in automation systems for condition monitoring:

• Integration of IoT-enabled Sensors:  In this step, critical automation systems are fitted with a wide range of IoT-enabled sensors, such as temperature sensors, pressure sensors, proximity sensors, flow sensors, vibration sensors, level sensors, force sensors, etc.,  depending on the type of automation device and the data needed for accurate predictions. These sensors collect the system data in real-time, providing a constant stream of detailed information regarding the operational state of the automation system. This real-time condition monitoring allows immediate detection of deviations from normal operating conditions.

• Data Acquisition: The gathered sensor data is sent to a centralized computing system like a cloud-based data analytics platform for processing and analysis.

• Data Preprocessing: The collected raw sensor data is oftentimes contaminated with noise or unnecessary information. Therefore, before analysis, this data has to undergo preprocessing, which involves tasks like data filtering, cleaning, transformation, and normalization to ensure uniformity and compatibility with predictive and analytical models.

• Feature Extraction: Relevant features from the preprocessed data are extracted to build a dataset that records the important properties of the automation system. Statistical measures, frequency domain analysis results, and other essential details can also be included.

• Machine Learning Analytical Models: Historical and real-time sensor data collected from the automation system is used to create and train advanced Machine Learning algorithms such as regression models, support vector machines, and deep learning models. These models learn the patterns and relationships between the extracted features and the health status of the automation system.

• Training and Validation: Machine learning models are trained using historical data encompassing normal and failure scenarios. Validation datasets are used to fine-tune the models and confirm that they can accurately forecast potential failures.

• Real-Time Monitoring: The trained analytical models are used to monitor the automation system’s performance trends continuously. As new data is acquired, the models continuously assess the health index of the automation system components and make forecasts about their remaining service life or likelihood of breakdown.

• Thresholds and Alarms: Key system indicators have predefined thresholds, and alarms are triggered whenever these thresholds are exceeded. In most cases, whenever faults are identified or the preset thresholds are exceeded, maintenance teams get notified, allowing for preemptive intervention.

• Decision Support Systems: Predictive analytics platforms are often integrated with decision support tools, providing maintenance teams with actionable insights. Based on the anticipated failure risks, these tools may recommend particular maintenance actions to be carried out, such as lubrication, component replacement, or alignment adjustments.

• Continuous Improvement: As new sensor data becomes available, the predictive algorithms are constantly enhanced; this involves upgrading the analytical models, re-evaluating feature sets, and incorporating the feedback from performed maintenance actions. In a nutshell, continuous improvement ensures that the analytical models maintain their effectiveness and readily adapt to changing operating conditions.

Fault Detection and Diagnostics

Machine Learning algorithms in predictive analytics are used to examine the historical and real-time operational data of a given automation system to identify failure patterns, consequently allowing for early detection of faults while also assisting in diagnosing the root causes. Various fault detection predictive models can be used to identify different types of possible system faults. These models may range from simple similarity-based failure threshold models to complex Machine-Learning predictive algorithms.

For example, anomaly detection models can spot unexpected patterns in data, whereas a fault signature analysis model can find indicators of a specific problem. When a deviation or anomaly is found, a diagnostic analysis is then carried out to determine the root cause of the problem. Correlating the observed deviations with known fault signatures or patterns allows for a more precise diagnosis in this use case.

Power Consumption Regulation

Predictive maintenance can be used to monitor the power consumption of an automation system. This monitoring extends beyond mere data collection to generate useful insights into power usage patterns, uncovering inefficiencies and forecasting future demands by continuously collecting and analyzing the system’s real-time power consumption data. Unusual power consumption patterns can indicate system inefficiencies or impending system failure.

Automation Software Health Management

Software Health Management (SHM) is the continuous monitoring, analysis, and optimization of software components within a system. It involves various tasks, such as real-time data collection, performance monitoring, defect identification, and diagnostics. 

All automation systems include software components, such as operating systems, user-defined programs, firmware, drivers, application programs, user interfaces, and middleware, which instruct, coordinate, and monitor the hardware components. Therefore, it is important to carry out SHM in such systems to address software issues more proactively, maximize the usage of software resources, and improve the overall health of the system’s software infrastructure.

Predictive maintenance algorithms can help implement more effective Software Health Management by tracking the health of an automation system’s software components and forecasting potential vulnerabilities, flaws, or updates.

Fluid Analysis

Predictive maintenance in automated hydraulic systems involves testing fluid samples for pollutants, wear particles, and other contaminants. The evaluation of hydraulic liquids such as oil, coolant, and hydraulic fluids that play critical roles in the operation of hydraulic-powered automation systems is called fluid analysis. Engineers and data scientists can obtain valuable insights into the status of an automation system, identify potential flaws early on, and predict when maintenance is necessary by carrying out fluid analysis of the system.

Fluid analysis in predictive analytics involves:

Oil Analysis

Wear Particles: Analysts can examine the state of gears, bearings, and other components of a hydraulic automation system by monitoring the quantities of wear particles in the hydraulic oil. An increase in wear particles may signify the onset of mechanical problems in the system.

The chemical composition of a given hydraulic oil can disclose pollution, oxidation, and the presence of additives. Changes in these parameters can indicate potential system faults.

Coolant Analysis

Corrosion and Contamination: Examining the coolant for pollutants, corrosion, and pH levels assists in identifying potential faults in the hydraulic cooling system. Note that corrosion and pollution can cause overheating and efficiency loss.

Hydraulic Fluid Analysis

Viscosity: Changes in viscosity may signify fluid degradation, which can compromise the performance of the hydraulic automation system. Monitoring the viscosity of the hydraulic fluid using various predictive maintenance tools can help determine when fluid replenishment will be required.

Particle Count: Tracking the amount and size of particles in a hydraulic fluid can assist in detecting wear on various components of the hydraulic automation system, such as pumps and valves.

Benefits of Implementing Predictive Maintenance in Automation Systems

• Cost Savings: Scheduled maintenance based on predictive insights reduces/eliminates the need for emergency repairs, unnecessary maintenance, and costly replacements of broken-down components. This results in cost savings due to optimized resource allocation and the extended lifespan of the automation system components.
• Data-driven decision-making: The vast amount of data collected andanalyzed during predictive maintenance activities yields valuable information about the monitored automation system. This helps generate data-driven insights that organizations can use to make well-informed and better decisions regarding system upgrades, resource allocation, and maintenance plans.
• Improved Operational Efficiency: Predictive maintenance enables enterprises toimprove the operational efficiency of their deployed automation systems, as it makes it possible for maintenance teams to address technical issues as they arise before they escalate into major breakdowns. This ensures smoother operations, improved overall efficiency, and increased productivity.
• Reduced Downtime: Predictive maintenance reduces unplanned downtime by allowing early detection and timely resolution of issues even before they occur. This ensures the continuous and reliable operation of a given automation system.
• Improved Worker Safety: Predictive maintenance practices help ensure that the components of an automation system are in optimal working condition, reducing the risk of workplace accidents and injuries.
• Optimized Maintenance Schedules: Predictive maintenance can provide precise insights into the condition of an automation system and recommend the best time to perform repairs or other maintenance tasks. This helps to optimize the timing of scheduled maintenance, with organizations undertaking maintenance tasks precisely when needed rather than sticking to predefined maintenance schedules. The result is maximized resource utilization.

This entry was posted on March 4th, 2024 and is filed under Automation, Education. Both comments and pings are currently closed.