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 depl...
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