Introduction to Equipment Health in Labs and Manufacturing
The role of ultra-low temperature (ULT) freezers in pharmaceutical, biopharmaceutical manufacturing, and medical device manufacturing is indispensable. These critical assets ensure the integrity of biological samples and temperature-sensitive materials, underpinning vital research and production processes. The introduction of the AI-Predicted Health Score by Elemental Machines marks a significant advancement in monitoring and maintaining the health of freezers and other lab equipment, leveraging the convergence of artificial intelligence (AI) and the Internet of Things (IoT) to transform maintenance approaches.1
The Urgency of Efficient Equipment Management
Managing a diverse array of lab equipment — from sequencers to centrifuges — is a formidable challenge. Unplanned downtime can have significant repercussions, including disrupted research activities and substantial financial losses. Predictive maintenance plays a key role in minimizing these impacts by forecasting potential failures, ensuring operational continuity, and extending equipment life span.2
In the face of global market growth and an increasingly competitive environment, the manufacturing industry is under pressure to seek continuous improvement. This has led to a focused effort on squeezing every asset for maximum value without significantly compromising system reliability or productivity. The introduction of intelligent maintenance systems (IMS) and predictive manufacturing approaches has emerged as a crucial advancement in minimizing unplanned downtime, assuring product quality, reducing customer dissatisfaction, and maintaining a competitive edge in the market. These systems provide decision support tools that optimize maintenance operations through intelligent prognostic and health management tools, imperative for consistent production with minimized unplanned downtime.3
The Role of AI in Predictive Equipment Maintenance
The AI-Predicted Health Score utilizes IoT sensors and proprietary AI algorithms to forecast equipment health and efficiency. This innovative approach enables laboratories to adopt more sustainable and efficient practices through:
- Frequent data collection: Every 15 seconds data on temperature and power consumption are gathered, offering a granular view of equipment performance.
- AI analysis: The algorithms analyze this data to identify early signs of potential failure, a capability refined through the analysis of vast datasets, underscoring the importance of early intervention.4
- Predictive alerts: By anticipating equipment issues, the Health Score enables proactive maintenance strategies, significantly reducing the likelihood of unplanned downtime and associated costs, while maximizing equipment life span.3
Advantages of Chill-Up and AI-Predicted Health Score
Implementing chill-up strategies for ULT freezers and leveraging the AI-Predicted Health Score results in enhanced energy efficiency and equipment health. These strategies not only reduce energy consumption but also extend freezer longevity and reduce maintenance needs, embodying the dual benefits of energy conservation and improved equipment durability.
Elemental Machines conducted a study to explore the potential of chill-up freezer set points and the impact of AI-Predicted Health Scores on energy consumption and freezer health:
- Chill-up freezer set points: Adjusting ULT freezers from the standard -80°C to -70°C demonstrated a remarkable 20% reduction in daily energy use, translating into significant cost savings without compromising sample integrity.
- Operational impact: The study revealed that newer freezers more effectively maintain chill-up temperatures, optimizing energy use; the AI-Predicted Health Score further aids in monitoring these adjustments, highlighting the reduced strain on compressors and extending equipment life span.
The application of AI in predictive maintenance has also shown significant promise in enhancing the operational efficiency of renewable energy systems. A case study focusing on the predictive maintenance of wind farms demonstrated that AI assistance improved the technical inspector’s specificity and time efficiency in identifying bearing faults through endoscopic images. This AI assistance had a statistically significant impact, improving specificity and time efficiency by 24.6% and 25.3%, respectively, for generalists (4.7% and 6.4%, respectively, for specialists). This illustrates the potential of AI to enhance energy efficiency and operational reliability in a diverse range of equipment, including ULT freezers in laboratory settings.5
Moreover, AI-driven predictive maintenance facilitates a shift from traditional maintenance approaches to more dynamic, proactive strategies. By analyzing historical maintenance records and real-time sensor data, AI algorithms can anticipate equipment failures before they occur, allowing for more effective resource allocation and mitigating downtime. This approach not only enhances equipment life span but also contributes to a more sustainable operational practice by optimizing energy use and minimizing waste, underscoring the transformative potential of AI and machine learning in predictive maintenance.6
Scalability and Security: Pillars of Asset Monitoring Software Solutions
Cloud-based software solutions emphasize scalability and security, ensuring they adapt to the evolving needs of modern labs and manufacturing environments without compromising data integrity or confidentiality. The dynamic nature of cloud computing allows these systems to scale resources efficiently, accommodating the fluctuating demands of data processing and storage. However, as these environments expand, so do the challenges associated with securing vast amounts of sensitive data against unauthorized access and cyber threats.
The integration of multi-cloud environments has necessitated the development of sophisticated security mechanisms to enforce data privacy and isolation while managing the risk associated with cloud services. The complexity of cloud architecture, involving multiple layers of software and infrastructure, requires a comprehensive approach to security that includes risk management, security-by-design, and regular vulnerability assessments. Furthermore, real-time monitoring and threat detection systems are pivotal in identifying potential attacks, underscoring the importance of a proactive security posture in cloud-based systems.7
In addressing these challenges, cloud-based security services have been developed to protect against attacks while maintaining the privacy of system states and user behavior. Such services involve the inspection of private system states, a process that must not disclose sensitive information to untrusted entities like cloud service providers. A framework for building privacy-preserving, cloud-based security services demonstrates the potential to maintain end-user privacy with an acceptable performance overhead, thus balancing the need for security with the imperative of user privacy.8
Seamless Integration and Comprehensive Monitoring
The comprehensive connection of laboratory and manufacturing systems with Laboratory Information Management Systems (LIMS), Electronic Lab Notebooks (ELNs), Quality Management Systems (QMS), and Computerized Maintenance Management Systems (CMMS) enables real-time monitoring of every lab asset. This unified approach not only ensures operational excellence but also offers detailed monitoring and informed decision-making across all activities. The integration of these systems into a cohesive monitoring platform allows for the continuous tracking of various parameters, enhancing the ability to maintain optimal conditions for experiments and production processes.
In the realm of environmental monitoring, the integration of sensor[1]based IoT technologies facilitates the precise and real-time collection of data pertaining to critical environmental variables within laboratories. This capability is pivotal for ensuring the accuracy of experimental results and the reliability of manufacturing processes, where conditions such as temperature, humidity, and the presence of contaminants can significantly impact outcomes.
Furthermore, an IoT-based smart laboratory environment monitoring system exemplifies the practical application of these technologies, demonstrating their value in automating multiple sensor readings and enabling remote data analysis and visualization. Such systems underscore the importance of a comprehensive monitoring strategy that leverages the latest technological advancements to enhance the efficiency and safety of laboratory and manufacturing environments.9
Elevating Labs With Strategic Partnerships
Strategic partnerships play a pivotal role in elevating laboratories to the forefront of innovation and efficiency, especially when integrating IoT-based solutions. By partnering with other industry leaders and innovators, a company can leverage diverse expertise and resources to develop a comprehensive ecosystem that supports the creation of fully trackable, smart lab environments. These partnerships not only broaden the range of available technological solutions, but also ensure that these solutions are compatible, scalable, and tailored to meet the unique needs of modern laboratories.
This holistic approach allows for the optimization of lab operations, from automating routine tasks to facilitating real-time data analysis and decision-making. Our ecosystem of strategic partnerships enhances lab capabilities by integrating software, hardware, and distribution networks to create a fully trackable, smart lab environment.
Looking Toward the Future: Beyond Freezers
Elemental Machines’ vision for the AI-Predicted Health Score extends well beyond the realm of ULT freezers, with ambitious plans to broaden the application of its technology to a wider array of laboratory and manufacturing equipment. This strategic expansion underscores the transformative potential of AI and IoT technologies in revolutionizing equipment management practices across diverse settings.
By leveraging the predictive capabilities and real-time monitoring afforded by AI and IoT, we aim to enhance the operational efficiency, reliability, and longevity of a vast spectrum of critical tools and machines. This approach not only promises to mitigate downtime and streamline maintenance processes but also fosters a more proactive and predictive maintenance culture.
A forward-looking strategy such as this reflects a commitment to harnessing cutting-edge technologies to deliver comprehensive, intelligent solutions that address the complex challenges of equipment management in modern lab and manufacturing environments. This expansion is geared toward realizing the full potential of digital transformation in these sectors, setting the stage for unprecedented levels of automation, precision, and insight into equipment health and performance.
Transforming Operational Potential with AI and IoT
Artificial intelligence and the Internet of Things technologies are transforming the very framework of how equipment maintenance is perceived and implemented. This shift is critical in today’s fast-paced, data-driven world where downtime not only means lost productivity but also potentially hampers critical research and development activities. By leveraging AI to predict equipment failures before they happen, the AI-Predicted Health Score ensures that laboratory operations can proceed uninterrupted, thereby safeguarding the integrity of scientific research and manufacturing outputs.7
Furthermore, the incorporation of IoT technologies into this predictive framework allows for the seamless gathering and analysis of vast amounts of operational data. This real-time data collection and analysis are pivotal in identifying not just potential equipment failures, but also in optimizing equipment performance and energy usage, contributing to more sustainable laboratory and manufacturing practices. The AI-Predicted Health Score, therefore, goes beyond maintenance, touching upon the broader goals of operational efficiency, sustainability, and the facilitation of high-quality, uninterrupted scientific inquiry and production.9
By continuing to extend the AI-Predicted Health Score technology across a wider array of equipment, Elemental Machines hopes to empower researchers and manufacturers with the tools they need to achieve breakthroughs, setting new standards in equipment management, and ensuring that laboratories and manufacturing facilities can operate at the pinnacle of efficiency, reliability, and sustainability.
References
- Achouch, M., et al. On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Applied Sciences. Published 2022 August 12.
- Karuppiah, K., et al. On sustainable predictive maintenance: Exploration of key barriers using an integrated approach. Sustainable Production and Consumption. Published 2021 July.
- Lee, J., et al. Intelligent Maintenance Systems and Predictive Manufacturing. Journal of Manufacturing Science and Engineering. Published 2020 August 18.
- Bousdekis, A., et al. Decision Making in Predictive Maintenance: Literature Review and Research Agenda for Industry 4.0. IFAC-PapersOnLine. Published 2019 January 1.
- Shin, W., et al. AI-assistance for predictive maintenance of renewable energy systems. Energy. Published 2021 April 15.
- Arpilleda, J.Y. Exploring the Potential of AI and Machine Learning in Predictive Maintenance of Electrical Systems. International Journal of Advanced Research in Science, Communication and Technology. Published 2023.
- Carvallo, P., et al. Multi-cloud Applications Security Monitoring. Green, Pervasive, and Cloud Computing: 12th International Conference, GPC 2017, Cetara, Italy, May 11-14, 2017, Proceedings 12. Published 2017 April 13.
- Chen, Y., et al. Preserving user query privacy in cloud-based security services. Journal of Computer Security. Published 2014 December 16.
- Samonte, M.J.C., et al. Internet-of-Things Based Smart Laboratory Environment Monitoring System. 2021 IEEE 8th International Conference on Industrial Engineering and Applications (ICIEA). Published 2021.
Author Details
Sridhar Iyengar, CIO & Founder- Elemental Machines
A serial entrepreneur in IoT, medical devices, and wearables, Sridhar Iyengar was a founder of Misfit, a maker of elegant wearable products acquired by Fossil in 2015. Prior to Misfit, he founded AgaMatrix based on his Ph.D. research, a blood glucose monitoring company that made the world’s first iPhone-connected medical device. Sridhar holds more than 50 U.S. and international patents and received his Ph.D. from Cambridge University as a Marshall Scholar.
Publication Details
This article appeared in Pharmaceutical Outsourcing:Vol. 25, No. 2Apr/May/Jun 2024Pages: 20-23