How to Leverage Predictive Maintenance to Reach Sustainability Goals
By The Maintainers • Read Time 1 min
As sustainability and environmental stewardship become more prevalent in our day-to-day, industries worldwide are striving to achieve net-zero emissions. Central to this effort is the adoption of innovative technologies and practices that reduce energy consumption and minimize environmental impact through decarbonization.
In recent years, technology has progressed enough to develop tools and strategies that optimize manufacturing and maintenance. One such tool is predictive maintenance, a forward-thinking approach that not only ensures optimal asset performance but also plays a role in the journey towards a net-zero future—an effort with significant cost savings (per a US Department of Energy study, maintenance techniques targeting energy efficiency can result in up to 20 percent savings annually)— as well as an invaluable effort to protect the environment.
Why Not Preventive Maintenance
Preventive maintenance, as the name suggests, is a maintenance strategy aimed to prevent problems and failures before they occur. While it has significant benefits and is a crucial way to optimize your maintenance strategy, reduce downtime, and minimize failure, oftentimes it does not provide a comprehensive enough view of your assets’ condition to fully stop a failure.
Contrast this with predictive maintenance, which utilizes condition monitoring tools like IoT sensors and integrated enterprise asset management (EAM) software, to constantly monitor your assets. Having a 24/7 view of your machinery enables you to make small fixes ahead of time when they are relatively less expensive than they can be later.
The Power of Predictive Maintenance
Predictive maintenance is a cutting-edge approach to asset management that leverages data analytics and technology to forecast when equipment is likely to fail. It goes beyond traditional reactive and preventive maintenance strategies, which often lead to unnecessary downtime or costly, preemptive replacements. Instead, predictive maintenance uses real-time data from sensors, historical performance records, and advanced algorithms to identify patterns and anomalies in the behavior of machinery and equipment. This allows maintenance teams to intervene proactively, addressing potential issues before they escalate into breakdowns.
One of the significant advantages of predictive maintenance is the level of transparency it provides to industries and plants about their assets. This encompasses several critical aspects:
Asset Health and Condition: Predictive maintenance programs provides a real-time assessment of the health and condition of assets. By continuously monitoring factors like vibration, temperature, and other performance indicators, maintenance teams gain a comprehensive understanding of how well their assets is functioning.
Early Fault Detection: Through advanced analytics, predictive maintenance identifies early signs of deterioration or impending equipment failures. This allows for timely intervention, preventing costly breakdowns and minimizing unplanned downtime.
Energy Consumption and Usage: Predictive maintenance can monitor energy consumption patterns of assets. By identifying inefficiencies or anomalies in energy usage, industries can implement corrective measures to optimize energy consumption, reduce waste, and ultimately lower operational costs.
Performance Optimization: With insights from predictive maintenance, industries can fine-tune the operational parameters of their assets. This ensures that equipment operates at peak efficiency, enhancing overall performance and productivity.
Resource Allocation Efficiency: By targeting maintenance efforts where they are needed most, predictive maintenance optimizes resource allocation. This means that maintenance teams focus their efforts on critical assets, reducing unnecessary interventions and maximizing the impact of maintenance activities.
Lifecycle Extension: By proactively addressing potential issues, predictive maintenance extends the lifespan of assets. This not only reduces the need for premature replacements but also contributes to sustainable resource management.
Data-Driven Decision-Making: Predictive maintenance provides industries with a wealth of data and insights about their assets. This information empowers decision-makers to make informed choices about maintenance strategies, equipment upgrades, and capital investments.
According to a study by the Industrial Internet Consortium (IIC), businesses utilizing predictive maintenance methods increased equipment uptime by 70% while reducing maintenance expenses by 30%. By avoiding unnecessary maintenance and unplanned downtime, businesses can save on maintenance expenses and increase production efficiency, contributing to their sustainability efforts. Predictive maintenance can also help businesses reduce their environmental footprint by optimizing their energy use and reducing emissions.
Reducing Energy Consumption and Optimizing Resource Allocation
One of the key drivers of achieving net-zero emissions is minimizing energy consumption. Predictive maintenance aids in this endeavor by ensuring that equipment operates at peak efficiency and with minimal loss. When preventive maintenance strategies are implemented, issues such as worn components or suboptimal performance are identified before they escalate. These problems can be rectified promptly, curtailing energy waste.
In addition, predictive maintenance allows for the optimization of energy-intensive processes by scheduling maintenance during off-peak hours or when renewable energy sources are most abundant. Not only does this reduce operational costs but also aligns with the broader goal of transitioning towards cleaner energy alternatives.
Predictive maintenance also revolutionizes how resources are allocated and used within an organization. Instead of a one-size-fits-all approach to maintenance, resources are directed where they are needed most. This targeted strategy minimizes unnecessary interventions and reduces the consumption of materials and spare parts. It also enhances workforce efficiency, allowing skilled technicians to focus on critical tasks rather than spending time on routine or unnecessary maintenance. This streamlined approach not only contributes to maintenance cost savings but also aligns with the sustainable resource management required for a net-zero future.
Per the International Energy Agency (IEA), implementing condition monitoring and predictive maintenance procedures can reduce energy use and greenhouse gas emissions by up to 10% in the energy industry.
Industrial Decarbonization through Predictive Maintenance
Decarbonization is the process of lowering or eliminating the greenhouse gasses that result from human activity, such as carbon dioxide. In the context of climate change, one of the most urgent environmental issues confronting the globe, decarbonization is a major aspect of attempts to attain sustainability. Burning fossil fuels for energy is one of the main causes of greenhouse gas emissions, and switching to cleaner energy sources like wind, solar, and hydropower is necessary to lessen our influence on the environment.
Industrial operations have a significant effect on global carbon emissions. Approximately 42% of the world’s greenhouse gas emissions are attributed to industry and energy production, per the Intergovernmental Panel on Climate Change (IPCC). This includes emissions from the creation and use of energy as well as emissions from commercial operations like the manufacture of cement, iron and steel, and chemicals. The 2020 Global CO2 Emissions Report from the International Energy Agency (IEA) found that energy-related CO2 emissions hit a new high of 33.1 gigatons in 2019, with around 40% of the total CO2 emissions caused by industry and energy production.
Rotating equipment is a significant source of industrial power consumption in the United States, with a majority of power usage attributed to such equipment. Misalignment, unbalance, and improper lubrication can cause extra vibration, energy consumption, and friction energy losses. This highlights the importance of effective condition based maintenance strategies (such as vibration analysis) to optimize the performance of rotating equipment and reduce energy consumption.
An industry study by EPRI found that optimizing the performance of rotating assets could lead to energy usage savings of 12–15%. Implementation of effective condition monitoring solutions allows companies to gain valuable insights into the performance of their assets, reduce energy consumption, and contribute to their sustainability goals.
TRACTIAN’s smart vibration and temperature monitoring sensor, Smart Trac, is the most advanced sensor on the market and is designed for continuous and online condition monitoring of rotating equipment. Backed by AI-powered insights and patented fault prescription technology, Smart Trac collects triaxial vibration and temperature data in real time, remotely and automatically every 5 minutes. Any machine behavior variation that might indicate a potential failure is identified, automatically generating a diagnosis and prescription.
Embracing Sustainability through Technological Integration
The adoption of predictive maintenance is inherently linked with technological advancement. Integrating IoT sensors, AI-driven analytics, and advanced monitoring systems empowers businesses to transition towards a smarter, more sustainable operational model. Through continuous data collection and analysis, companies can gain deep insights into their assets’ health and performance, allowing for informed decision-making. This technological integration not only enhances operational efficiency but also positions businesses to be at the forefront of sustainable practices.