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#015: Can your PM do this? - Part 1
Is your PM predictably intelligent? Introducing ContinuousPdM!
Nagesh Nama, Jas Patil & Param Nagda
June 08, 2024 • Estimated Reading Time: 18 minutes
#015: Can your PM do this?
Is your PM predictably intelligent? Introducing ContinuousPdM!
Credits: Stability.AI
Table of Contents
The birth of ContinuousPdM - Intelligent Predictive Maintenance……..
Ram and Rob on ContinuousTV
Credits: Stability.AI
Once upon a recent time, a talented xLM AI-ML Engineer was on an assigment to change a customer's antiquated PM program to a modern, intelligent, data driven, predictive program.
Here is the conversation that ensued:
Ram (xLM AI/ML Engineer): Good morning, Rob I am here to breathe some fresh air into your PM program.
Rob: Why? What? My PM program is just fine. Has been for many years now.
Ram: When you say fine, you really mean it seriously, right?
Rob: Of course! We do everything by the book on a weekly, monthly, quarterly, yearly......
Ram: Great! Does that mean there are absolutely no line failures and no line downtime?
Rob: That is not my area. I am in PM.
Ram: If PM is really working, why are there major failures? Let me ask you this. Is your PM data driven?
Rob: Nope. It is fixed schedule driven.
Ram: I know you guys collect a lot of data. How come it is not tied into the PM program.
Rob: What has data to do with PMs. PMs are done on a periodic basis.
Ram: Then how do you know that the PMs are working effectively?
Rob: Again PMs are done on schedule, every week, month, quarter, year.
Ram: (Turning to the audience now) Preventive needs to move to an intelligent, data driven, ROI based, predictive system. PREVENTIVE is just bad business. It is like working without knowing how much one will get paid. Maintenance is meant to maintain the uptime with intelligence based on data. Introducing ContinuousPdM (Intelligent Predictive Maintenance not "preventative") The PM Background
“Preventive” is so glued into our work culture that it literally means doing certain “useful” tasks on a periodic schedule not really worried about ROI or Line Downtime. In fact, the PM schedules cause a lot of line downtime. Lines like to run all the time. They don’t like to take a rest while someone opens up their gut and start running again. A high speed mass manufacturing line growls and literally cries when it hears the word “PM”. Hey, PM is supposed to give the line a healing touch. Does it really? In fact it adds to the downtime and most lines take a long time to recover before they really wake up and start running at full speed.
A line stuttering and growling!
Let us step back and ask ourselves why are we doing something that is so expensive time and again, and so religiously without questioniong the ROI or the logic behind all the PM tasks. Does it make any engineering sense?
Doesn’t it make sense to identify the root causes of the most expensive line burnouts in any given year and make sure such a thing does not happen again. How is this possible? One of the trains to get on is intelligent predictive maintenance.
Predictive Maintenance (PdM)
Predictive Maintenance is a proactive approach to maintaining assets and equipment by using data analytics and machine learning to detect potential failures before they occur.
Manufacturing companies can monitor sensor data from production machinery and equipment. By analyzing historical data on equipment performance, vibration patterns, temperature readings, and other metrics, an intelligent platform can build predictive models to forecast when components are likely to fail or require maintenance. This allows manufacturers to schedule maintenance activities during planned downtimes, reducing unplanned outages and increasing asset utilization.
In the manufacturing sector, AI and ML have proven to be game-changers for predictive maintenance, enabling organizations to optimize asset performance, reduce downtime, and improve overall operational efficiency.
Here are some compelling use cases that showcase the power of AI and ML in predictive maintenance for manufacturing:
Predictive Failure Analysis for Critical Machinery
AI and ML algorithms analyze historical data from sensors, maintenance logs, and operational parameters to predict when critical machinery or equipment is likely to fail. This allows manufacturers to schedule maintenance proactively, reducing unplanned downtime and associated costs. For example, AI can detect anomalies in vibration patterns or temperature readings, indicating potential bearing failures or overheating issues in production lines.
Condition Monitoring for Automated Assembly Lines
Automated assembly lines are the backbone of many manufacturing processes. AI-powered condition monitoring continuously analyzes sensor data from robotic arms, conveyors, and other automated systems to identify deviations from normal operating conditions. This early warning system enables maintenance teams to address potential issues before they lead to failures, minimizing disruptions and ensuring smooth production flows.
Predictive Quality Control
AI and ML can be applied to predict quality issues in manufacturing processes by analyzing data from various sources, such as sensor readings, process parameters, and historical quality data. This predictive quality control approach enables manufacturers to identify and address potential quality issues before they occur, reducing waste, rework, and customer complaints.
Optimized Maintenance Scheduling
AI and ML models can analyze data from multiple sources, including maintenance logs, sensor data, and production schedules, to optimize maintenance schedules for various assets and equipment. This approach ensures that maintenance activities are performed at the most appropriate times, minimizing disruptions to production while maximizing asset availability and lifespan.
Predictive Inventory Management
By leveraging AI and ML to predict equipment failures and maintenance needs, manufacturers can optimize their inventory management processes. This includes forecasting spare parts requirements, ensuring timely procurement, and minimizing excess inventory, ultimately reducing costs and improving operational efficiency.
Root Cause Analysis
AI and ML can assist in root cause analysis by identifying patterns and correlations in data from various sources, such as sensor readings, maintenance logs, and operational parameters. This helps manufacturers pinpoint the underlying causes of equipment failures or quality issues, enabling them to implement effective corrective and preventive actions.
By embracing AI and ML for predictive maintenance, manufacturers can transition from reactive to proactive maintenance strategies, optimizing asset performance, reducing downtime, and improving overall operational efficiency, ultimately leading to increased productivity and profitability.
How to implement an Intelligent Predictive Maintenance Program
1. Establish a Cross-Functional Team
Begin by assembling a cross-functional team that includes representatives from various departments, such as operations, maintenance, engineering, IT, and data analytics. This team will be responsible for driving the predictive maintenance initiative and ensuring its successful implementation.
2. Conduct a Comprehensive Asset Assessment
Identify and catalog all critical assets, including automated production lines, machinery, and equipment. Gather information about their age, condition, maintenance history, and criticality to operations. This assessment will help prioritize assets for predictive maintenance implementation. It is very important to prioritize based on the cost of downtime if a particular asset or a componenet fails.
3. Implement Sensor Infrastructure
Install (if needed) sensors and data acquisition systems on critical assets to collect real-time data on various parameters, such as vibration, temperature, pressure, and performance metrics. This data will serve as the foundation for predictive analytics.
4. Integrate Data Sources
Consolidate data from various sources, including sensors, maintenance and repair logs, production records, and enterprise systems (e.g., ERP, CMMS). Ensure data integrity, standardization, and compatibility across different systems.
5. Develop Predictive Models
Leverage machine learning and advanced analytics techniques to develop predictive models that can identify patterns, anomalies, and early warning signs of potential failures. These models should be tailored to specific asset types and operating conditions.
6. Establish a Centralized Monitoring System
Implement a centralized monitoring system that integrates data from various sources and predictive models. This system should provide real-time visibility into asset health, generate alerts, and recommend maintenance actions.
7. Develop Maintenance Strategies
Based on the predictive insights, develop proactive maintenance strategies, such as condition-based maintenance, risk-based maintenance, and predictive maintenance. Define clear procedures, workflows, and responsibilities for executing these strategies.
8. Train Personnel
Provide comprehensive training to maintenance technicians, operators, and other relevant personnel on the predictive maintenance program, data interpretation, and maintenance procedures. Ensure they understand the benefits and their roles in the program's success.
9. Continuously Monitor and Optimize
Continuously monitor the performance of the predictive maintenance program, track key performance indicators (KPIs), and make necessary adjustments to improve accuracy, efficiency, and cost-effectiveness. Today’s AI models are smarter and can incorporate reinforced learning.
10. Establish Governance and Change Management
Implement a robust governance structure to oversee the predictive maintenance program, ensure compliance with regulatory requirements, and manage changes effectively. This includes establishing policies, procedures, and regular reviews.
Predictive Model Development: An Use Case
In the highly competitive realm of manufacturing, predictive maintenance plays a crucial role in boosting operational efficiency, reducing downtime, and prolonging the lifespan of essential assets.
Let's delve into how manufacturers can harness the power of data science and machine learning to develop reliable predictive maintenance systems, accompanied by a practical use case:
Use Case: Predictive Maintenance for Conveyor System Bearings
Background: A manufacturing plant experiences frequent unplanned downtimes due to bearing failures in its conveyor systems. These downtimes result in significant productivity losses and high maintenance costs.
Objective: Develop a predictive maintenance system to forecast bearing failures and schedule maintenance proactively.
Integrate Diverse Data Sources
To conduct a thorough analysis and create predictive models, it is essential to integrate a variety of data sources. These sources may encompass machine-level sensors, maintenance logs, operational parameters, and historical performance records from platforms like CMMS, Historians, etc..
This extensive data compilation plays a vital role in pinpointing pertinent features and variables, offering solid evidence to support any solutions formulated from the analysis.
Data Sources:
Sensors: Vibration (Hz), Temperature (°C), and Operational Load (kg).
Maintenance Logs: Records of past bearing replacements and failures.
Operational Parameters: Conveyor speed (m/s), operational hours, and load.
Assemble a Specialized Team
Forming a team comprising data scientists and subject matter experts with a wide range of skills in machine learning, manufacturing, and operations is essential.
This interdisciplinary team will work together to pinpoint pertinent features and variables from the consolidated data outlets and create precise predictive models. The amalgamation of their expertise guarantees that the models are not only technically robust but also feasible in real-world scenarios.
Steps for Predictive Modeling
Understand the Use Case: Clearly defining the specific problem or use case is crucial for operational contexts like a manufacturing plant aiming to predict and prevent bearing failures in its conveyor systems. This involves a deep understanding of the equipment types, operational environments, and potential failure modes they may encounter.
Gather Data: Collecting data from a variety of sources is crucial for comprehensive analysis. This can involve information gathered from sensors measuring parameters like vibration and temperature, maintenance logs detailing previous bearing failures, operational data such as load and speed, and historical performance records like those stored in a Computerized Maintenance Management System (CMMS).
An instance of this data could encompass vibration readings measured in Hertz, temperature recorded in Celsius, and operational hours logged.
Clean and Prepare Data: To ensure the data is well-prepared for analysis, it is crucial to focus on cleanliness, consistency, and proper formatting. This process includes eliminating anomalies (such as unusual temperature spikes that do not align with real failures), filling in any missing values (utilizing interpolation techniques), and standardizing the data (scaling vibration measurements to a uniform range).
Perform Feature Engineering: Identifying and establishing pertinent features is crucial for predictive models. For instance, in the scenario of bearing failure, relevant features could encompass the average vibration frequency, temperature variance, and operational load. Feature engineering is a process that revolves around choosing variables that hold substantial influence over equipment performance and potential failure modes.
Apply Machine Learning Techniques: Apply supervised and unsupervised learning methods according to the specific use case. In supervised learning, labeled data (such as instances of previous bearing failures) is utilized to train models. On the other hand, unsupervised learning is capable of recognizing patterns in operational data even in the absence of predefined labels. For instance, a supervised learning model could leverage historical data to forecast the likelihood of bearing failure occurring within the upcoming 100 operational hours.
Validate the Model: Validate the predictive models by leveraging historical data and real-world scenarios. Utilize cross-validation methods (e.g., k-fold cross-validation) to guarantee the accuracy and resilience of the model. Define performance metrics such as precision, recall, and the F1 score to evaluate the model's efficacy. For instance, a model could demonstrate a 90% precision rate in forecasting bearing failures.
Deploy and Monitor the Model: Implementing the predictive model in a practical environment involves integrating it with the plant's monitoring systems. It is crucial to consistently monitor its performance and make necessary adjustments. Regularly updating the model with fresh data is essential to uphold its accuracy and relevance. An example scenario could be deploying the model to send alerts when the likelihood of bearing failure surpasses 70%.
Run Scheduled Maintenance: Integrating fresh data into the model regularly, followed by re-running and validating the model, and deploying updated versions are essential steps to maintain continuous accuracy and reliability. This iterative process plays a crucial role in ensuring the effectiveness and relevance of the predictive maintenance system. Routine updates may include re-training the model every 15 days using the most recent operational data.
Predictive Maintenance Examples
Cleanroom HVAC Systems
Predictive maintenance can be applied to HVAC systems in cleanrooms, which are critical for maintaining the required environmental conditions (temperature, humidity, particulate levels) for pharmaceutical manufacturing.
By monitoring factors like filter efficiency, motor performance, and potential HVAC component failures, predictive maintenance can ensure the sterile environment is not compromised, preventing product contamination and quality issues.
Lyophilization (Freeze-Drying) Equipment
Lyophilization is a crucial process for stabilizing temperature-sensitive pharmaceutical products. Predictive maintenance can monitor freeze-dryer performance by analyzing data like vacuum levels, shelf temperatures, and condenser performance.
Early detection of issues like seal degradation or compressor problems ensures reliable operation of this critical equipment, preserving the efficacy of pharmaceutical products.
Tablet Press Machines
Tablet presses are essential for producing solid-dose medications in precise dimensions and weights. Predictive maintenance to monitor parameters like punch and die wear, turret alignment, and compression force.
This proactive approach helps schedule maintenance before worn components affect tablet quality or cause production downtimes.
Chromatography and Mass Spectrometry Systems
Analytical instruments like chromatography systems (HPLC, GC) and mass spectrometers are vital for assessing drug purity and potency in pharmaceutical laboratories.
Predictive maintenance to monitor column performance, pump functionality, detector sensitivity, vacuum systems, and ionization sources in these instruments to ensure accurate and reproducible analytical results.
Packaging Lines
Predictive maintenance for packaging equipment like blister packaging machines, cartoners, and labelers. By monitoring factors like conveyor systems, faulty sensors, and sealing integrity, potential issues can be addressed proactively, minimizing disruptions and defects in the critical final packaging stage.
ContinuousPdM - Delivered as a Managed Service
In every service we offer, the software app is continuously qualified. Also the customer's instance is continuously validated. In each run, 100% regression is performed. In the case of ContinuousPdM, test data will be introduced continuously to validate the model’s output.
Conclusion
Preventive Maintenance is definitely not the answer if you are serious about increasing production efficiencies, reducing downtime and cost. Logical maintenance is the answer. Get onboarded on to ContinuousIPM - your data driven, AI infused, logical answer to cost efficiencies.
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