Harnessing AI for Smarter Equipment Monitoring and Maintenance in the Pharmaceutical Industry

The reliability of equipment in the pharma manufacturing process is critical for the success of the product and the production department. It helps pharma manufacturers achieve product quality, safety, and productivity targets. 

The key to reliable equipment operation is an effective monitoring & maintenance strategy that is capable of timely detecting root cause, differentiates between personnel fault & component malfunction, and implements timely Maintenance to prevent alteration in product properties and production delays.      

Shift from traditional based techniques

Pharmaceutical manufacturing operations are technically complex and require proactive monitoring and Maintenance to ensure product quality and enhance productivity. Most pharma organizations focus on traditional monitoring & maintenance methods, which are insufficient to cater for the latest pharma manufacturing requirements and productivity targets.

Traditional methods cannot timely detect faults in an ongoing production process, differentiate between component malfunction & human error, and execute Maintenance in less time. The recent shift in monitoring & Maintenance is using AI for equipment maintenance, which is becoming a game changer. They can detect failure promptly and effectively plan maintenance schedules, leading to cost savings and improved productivity. 

Equipment Monitoring & Maintenance in the Pharmaceutical Industry

The pharmaceutical industry consists of various equipment for different production processes. It includes HVAC, autoclaves, filling lines, and packaging, designed to execute their designated function. The goal of each piece of equipment is to produce the given number of products in a given time with the intended product quality. However, this is not often the case, and equipment faces faults in its various components, causing it to break down. 

To prevent faults and equipment breakdown, the pharma industry relies on equipment monitoring & maintenance plans to prevent, detect, and rectify faults in its equipment. An effective maintenance plan & strategy help to remedy these issues and to avoid recurrence.

Since the focus of this article is AI, we will discuss role of AI in monitoring and maintenance. Lets start with AI in equipment monitoring

AI in Equipment Monitoring 

The AI has been matured enough to gather meaningful data to monitor equipment status. The AI in equipment can be divided into Data Collection and Data Analysis

Data Collection

Data collection is the process of collecting real-time data related to individual components, processes, and machine outputs. It provides insight into the working, hardware, software and health condition. This contrasts with traditional methods, where data collection is only limited to few variables, that are insufficient to deduce any reliable conclusion. 

Real-time data collection includes the following.

Temperature monitoring—Temperature directly indicates a machine's characteristics, and monitoring can help detect anomalies within different machine components. An excessive temperature rise is likely due to abnormal conditions, such as overheating, friction, or a fault in the cooling system. The abnormal condition in the machine is directly related to faults in individual machine components, such as bearings, motors, and lubrication. 

Vibration monitoring—Moving and rotating parts are common components of equipment, and an anomaly induces abnormal vibration in these components. The vibration indicates a wear in rotating or moving parts because, by design and construction, they do not have tolerance. Any wear increases their tolerance, which results in vibration. Vibration monitoring enables personnel to detect these vibrations and alert if they cross the acceptable threshold.

In addition to these monitoring, other sensors, such as ultrasonic, pressure, and gas are also deployed depending upon the machine working and fault situations.

Data Analysis

After collecting data from various sources, it is analyzed for deducing anomalies, fault trends, and problems. Machine Learning is a technique used for fault analysis, and can help automate equipment monitoring in the following ways.

Analyzing a Large Volume of Data

AI-based data collection generates a large volume of data that becomes impossible for humans to analyze and make decisions. Additionally, it is also possible that humans could lose any dataset due to the reasons mentioned above. 

The AI based data analysis automates the monitoring, and can analyze at high speed. It also enables higher accuracy without losing any dataset.

Detecting Anomalies

Machine Learning algorithms can detect anomaly patterns from large volumes of data collected. The pre-set algorithm developed according to the client or application requirement automatically marks abnormal behavior and alerts concerned personnel. 

AI-enabled Maintenance

After collecting & analyzing the collected data, the next stage is to develop a maintenance strategy using collected and analyzed data. For this purpose, AI can be integrated to plan and execute Maintenance activities. The AI can plan maintenance activities before the equipment runs into a complete breakdown.

Planning maintenance before equipment degradation

AI can be implemented to initiate Maintenance before the degradation and failure of a single component or entire equipment. This technology is known as predictive Maintenance.

Predictive models take inputs from the algorithm & datasets, and are pre-defined to forecast equipment performance. They continuously analyze each component or component part's performance and detect if its performance, output, or working falls below threshold levels. The threshold level is pre-adjusted based acceptance criteria. Below the threshold values, the algorithm will flag a particular component as faulty and trigger maintenance initiation. 

Maintenance based on actual performance

The traditional practice is to plan Maintenance at fixed intervals, no matter what the state of the equipment is. It could either not require Maintenance or be in dire need of it. In both cases, Maintenance cannot be fully optimized. 

AI-based techniques enable planning Maintenance based on real-time data collection, not just fixed schedules. They continuously monitor the real-time data, and when an anomaly happens, it alerts relevant maintenance personnel. In predictive Maintenance, time interval or frequency has no role, which increases maintenance efficiency.

Integration with existing maintenance management software

Industries worldwide are increasing the use of maintenance management software, such as Computerized Maintenance Management System. It keeps track of the maintenance activities throughout the facility by maintaining a record of faults, Maintenance performed, and several features. 

The traditional method involves manual data collection and record-keeping. AI-based Maintenance, i.e. predictive Maintenance, can be easily integrated with variety of CMMS. 

The CMMS can be connected to a centralized data repository, such as cloud storage. If an AI system detects an anomaly, the CMMS automatically triggers an alert, which maintenance personnel can use to execute maintenance activity. 

The CMMS stores all the records related to equipment faults and Maintenance performed, for later use for analysis or regulatory purposes. 

Benefits of AI-enabled Maintenance & Monitoring

Some benefits of AI-based Maintenance & Monitoring include the following

Improved Overall Equipment Effectiveness - OEE

Overall Equipment Effectiveness: OEE is a standard in manufacturing industries that measures the productivity of equipment, which in turn depends on variety of factors. AI-based equipment maintenance and monitoring help improve OEE by increasing the total time of equipment running and reducing breakdown hours. 

The OEE can benefit in the following ways.

· Predictive Maintenance enables personnel to stop a machine only when a fault or issue occurs. It prevents unnecessary stoppages, such as in preventive Maintenance. Additionally, detecting anomalies promptly and preventing a major breakdown increases the machine's running time.

· The above feature also reduces un-necessary maintenance costs, such as spare parts and outsourced activities. The predictive maintenance is able to pinpoint the fault, which helps machine owners to procure only relevant spare parts, preventing un - necessary inventory build-up and associated costs. 

· Detecting faults in machine components increases the equipment's life span. Unnoticed defects often cause significant damage if they are not rectified promptly.

Regulatory Compliance

AI-based systems can help pharmaceutical manufacturers achieve regulatory compliance by minimizing product variations and quality compromises. Well-maintained equipment always results in quality and safe processes and products, and AI-based monitoring and maintenance systems enhance the capability of equipment to produce these characteristics. 

· Utilizing data to predict issues in machines, pharma manufacturers can ensure the desired product characteristics and GMP compliance. 

· Reduced breakdown enables consistent & reliable product process.

· AI-based systems enhance the equipment reliability and result in consistent product quality throughout the entire batch.

· Proactive risk resolution allows for the reduction of the risk of deviation from GMP compliance.

Record Keeping

Logging different physical variables, process parameters and maintaining records is an integral requirement for the pharmaceutical industry. Traditional methods involve operators manually logging values and keeping records. For obvious reasons, manual methods are prone to errors, and regulatory bodies are also encouraging pharmaceutical manufacturers to shift to automated systems. 

AI-based systems enable pharma manufacturers to implement automated record systems. 

· Data collected from different sensors can be used to keep relevant records, such as temperature and process parameters. 

· Data can be conditioned through software analytics to transform into various tables & formats.

· The recording process can be processed to follow different regulatory frameworks, such as Electronic Signatures, for safety and privacy issues.

Preventing Product Recalls

Product recalls in the pharmaceutical industry occur when products do not meet relevant quality and safety standards. These recalls are costly, and every pharmaceutical manufacturer tries to prevent them. AI-based equipment monitoring and Maintenance can be used to avoid product recalls.

Early anomaly detection using different sensor data enables manufacturers to adjust batch parameters before distributing them to the market. 

About the Author:

Muhammad Asim Niazi has a vast experience of about 11 years in a Pharmaceutical company. During his tenure he worked in their different departments and had been part of many initiatives within the company. He now uses his experience and skill to write interested content for audiences at PharmaSources.com.