Traditionally, maintenance professionals have integrated both quantitative and qualitative methods to forecast upcoming failures and minimize downtime in their manufacturing facilities. Predictive maintenance (PdM) presents an opportunity to improve maintenance activities in real time, enhancing the lifespan of equipment and preventing disruptions in operations. A key aim of every maintenance organization is to increase asset availability. In this piece, we delve into how PdM can be effectively utilized for fixed assets in manufacturing or warehouse automation settings. This approach is applicable to various types of facilities, such as manufacturing shop floors, warehouses, sortation facilities, as well as assets utilized in mining or medical environments.

The run-to-failure maintenance approach can result in severe asset damage as components start to vibrate, overheat, and ultimately break, underscoring the importance of addressing issues sooner rather than later. While this strategy may be suitable for certain assets, the unexpected downtime when assets fail can be costly and time-consuming. On the other hand, a preventive maintenance approach, which involves regularly replacing parts and servicing equipment, can lead to increased replacement costs and planned downtime. Predictive maintenance offers a balance between these two strategies by minimizing unnecessary preventive maintenance while preventing catastrophic failures.

Maximizing the Potential of Smart Factory Technology

The goal of Predictive Maintenance (PdM) is to empower businesses to extend the lifespan of their equipment components, minimize the risk of unexpected downtime, and reduce scheduled downtime. With the rise of Industry 4.0 in the manufacturing sector, companies have the opportunity to utilize advanced technologies to monitor their operations in real-time, allowing for improved production efficiency and cost savings. In essence, a smart facility is one that is equipped with technology that enables seamless communication between assets (machine to machine, or M2M) and between assets and humans (machine to human, or M2H), alongside the integration of analytical and cognitive technologies. This integration ensures that decisions regarding the facility are driven by data and made promptly.

The concept of Predictive Maintenance (PdM) has been established for many years and revolves around the use of data sourced from various platforms such as critical equipment sensors, programmable logic controllers (PLCs), smart electronic devices, enterprise resource planning (ERP) systems, computerized maintenance management systems (CMMS), and manufacturing execution systems (MES). Smart facility management systems integrate this data with advanced prediction models and analytical tools to forecast failures and proactively address them. In comparison, traditional maintenance approaches, which involve a trade-off between run-to-failure or preventive maintenance, often necessitate labor-intensive, manual data analysis to derive insights from collected data. While some organizations have achieved success with these methods, they typically rely heavily on subjective estimates or require ongoing comprehensive equipment knowledge to maintain accuracy.

Transitioning to Predictive Maintenance

In various industries, maintenance organizations can be found at different stages of maturity, whether intentionally or passively. Some may adhere to regular maintenance checks based on estimates or recommendations from the original equipment manufacturer (OEM), while others may utilize data-driven programs customized for each asset. Additionally, there are those who have adopted continuous monitoring technologies, though they may only analyze signals individually (univariate analysis) rather than capitalizing on predictive models. Embarking on the path towards optimizing reliability requires a strategic approach, starting with fundamental practices such as preventive maintenance and reliability-centered maintenance. It is also beneficial to conduct pilot programs for Predictive Maintenance (PdM) with a select few assets or facilities. These chosen assets should play a critical role in operations and have sufficient operating time to develop foundational predictive algorithms.

Several technologies in smart facilities may not be new, but they have become more cost-effective, durable, and compatible with big data platforms. Computing, storage, and network bandwidth are now more affordable than they were 20 years ago, making it financially viable to pilot and scale these technologies. These advancements in technology are key components in smart facilities that enable predictive maintenance.

Internet of Things (IoT)

IoT devices leverage internet infrastructure to transmit continuous data, typically generated in a continuous flow, from assets to private enterprise servers. Sensors like temperature, vibration, ultrasound, or conductivity are employed by IoT to convert physical movements from assets into digital signals. In addition to this, data can also be sent from various sources such as an asset’s PLC, MES terminals, CMMS, or even an ERP system. IoT plays a crucial role in completing the initial phase of the physical-digital-physical (PDP) loop. Due to the cost-effectiveness of bandwidth and storage, substantial volumes of data can be sent to provide comprehensive insights not only into assets within a single plant/facility but also across the entire production network.

Analytics and Visualization

The next stage of the PDP loop involves conducting an in-depth analysis and visualization of digital signals utilizing sophisticated analytics, predictive algorithms, and business intelligence (BI) resources. Various analytics platforms are equipped to process unstructured data, employ cognitive technologies, facilitate asset learning, and provide visualization capabilities. Operations analysts, with a firm understanding of manufacturing operations, can develop user-friendly dashboards utilizing modern application program interfaces (APIs) tailored for the average user. An emerging trend involves data shifting towards the edge of networks. Similar to storing tools at the point of use, data processing is now taking place at the "edge," where it is originally generated. This allows for real-time insights to be delivered to asset operators and maintenance technicians. Following edge processing, data may then be transferred to outer nodes or a cloud-based data warehouse to ease the strain on edge and core networks and enhance application performance.

Completing Cycle

Once the signals are processed, analyzed, and visualized, it is time to translate those insights into physical actions. Depending on the situation, these digital findings may prompt robots or assets to adjust their operations or trigger maintenance alerts that prompt technician intervention. For example, predictive algorithms can automatically generate a maintenance work order in the company's CMMS, check the ERP system for available parts, and initiate a purchase request for any additional components needed. The maintenance manager can then review and approve the items in the workflow and assign the appropriate technician before any machine failure occurs.

Potential Benefits

Initially, embarking on a Predictive Maintenance (PdM) program may appear daunting. However, the advantages of implementing digital transformation far surpass the initial challenges. These benefits encompass:

• Lower material costs (operations and maintenance, repair, and operations material expenditures)
• Decreased inventory holding costs
• Enhanced equipment uptime and availability
• Reduced maintenance planning time
• Lower overall maintenance expenses
• Improved health, safety, and environmental (HSE) compliance
• Less time devoted to manual data extraction and validation
• More time dedicated to data-driven issue resolution
• Clearly defined connections to initiatives, performance, and accountability
• Greater confidence in data and information leading to taking ownership of decisions.

Key Adoption Challenges

The rapid growth of industrial automation can be attributed to the development of IoT technologies, reduced costs of data storage and computing, and advancements in artificial intelligence and machine learning capabilities. However, maintenance organizations have not fully utilized these technologies beyond pilot projects due to the following reasons:

• Lack of a holistic vision
• Poorly defined business case
• Increasing technological complexity
• Inadequate change management

It is crucial for organizations to address these challenges in order to fully harness the power of emerging technologies in industrial automation.

How can Nirmalya Enterprise Asset Management Help?

Nirmalya EAM for industries provides a range of asset lifecycle, maintenance, and analytical solutions customized for various industries. Boost productivity, schedule maintenance, optimize asset performance, and reduce operational downtime with the convenience of web and mobile applications. Access your assets remotely at any time and from any location to add value. For more information on our enterprise solutions, please reach out to us.