Traditional manufacturing industries are in the throes of a digital transformation that will impact every aspect of the manufacturing process. Powered by rapid development in AI, IoT, Edge Computing and 5G, the Industry 4.0 revolution is realigning people, technology, data, services and products around a new manufacturing logic.
While there are numerous leading use cases for the adoption of AI in manufacturing, predictive maintenance seems to have the greatest potential to reduce costs and improve operational efficiencies. In fact, predictive maintenance is widely considered to be the logical next step for any manufacturing business operating complex, high-capital machinery. AI in predictive maintenance enables manufacturers to anticipate when something is about to go wrong and take steps to ensure it doesn’t. It is quickly becoming the cornerstone of Industry 4.0 and is widely applicable across the manufacturing sector.
What is predictive maintenance?
Predictive maintenance is a method of proactively anticipating risk factors that can result in failure or downtime of mission-critical manufacturing equipment, and preventing these failures before they happen. It is based on constantly analyzing data from multiple sensors and indicators throughout the factory floor, combining this data together, and using AI to pinpoint unusual or anomalous parameters indicating high probability of evolving into a machinery failure. Using these probabilities, factories can run timely maintenance repairs on each of their assembly line machines, without reaching a point at which the machine breaks down.
Up until recently, factories would prevent machinery failures by scheduling maintenance work at preset intervals. This methodology, referred to as Preventive Maintenance, relies mainly on visual inspections. While it helps keep machines running, it’s far from efficient at optimizing factory resources: half of all manually scheduled machine maintenance, across manufacturing verticals, is actually considered futile as it takes up a tremendous amount of resources, time and productivity on the one hand, but fails to deliver on the promise of minimizing downtime on the other.
Predictive maintenance uses an altogether different, analytical and data-based approach to maintenance, utilizing real-time and historical data to highlight where a machine is not performing as it should and repair it in advance. Utilizing AI for predictive maintenance enables manufacturers to monitor the condition of machinery on the production line, streamline maintenance schedules, and prevent breakdowns. This results in significant decrease in maintenance costs, while maximizing output and improving overall product quality.
How AI in predictive maintenance works
The potential for using AI-powered predictive maintenance in manufacturing is almost unlimited. It can be applied across verticals, production lines and machinery. Here are just a few specific examples:
- Detecting fragile spindles in milling machines using vibration sensors to identify patterns
- Identifying first signs of clogging in heat exchangers by detecting temperature differences between upstream and downstream flows
- Calling in specific vehicles from large car fleets for a tune-up, based on sensors that collect data and relay information on vehicle performance
- Monitoring inflight conditions of commercial jet engines by measuring various temperature and vibration levels
The first step in moving toward predictive maintenance is mapping the manufacturer’s unique pain points (e.g. drivers of costs, waste, or inefficiency) and needs (e.g. minimizing downtime, streamlining maintenance schedules, avoiding malfunctions), and focusing on the use case that will drive the most significant value for your business.
Predictive maintenance relies on AI and on the availability of big data. That’s where IoT plays a major role in providing, storing and processing machine data received from a myriad of sources: sensors, manufacturing execution systems, management systems, parts composition, historical equipment usage data, and more. This machine data is cross checked against additional sources, such as manual data from human inspection, static data (such as manufacturer service recommendations for each asset), and data from external APIs (weather conditions that can impact equipment, for example). It is precisely this combination of a variety of sources and data types that allows for the most robust and accurate predictive models. The more sources and data type available, the more accurate the prediction on the future operability of each asset in the factory.
After defining and gathering the relevant data sets that can impact machinery performance, it’s time to build the AI predictive model around them. This requires unique, cutting edge algorithmic and data science skills. Deploying a predictive maintenance model into production requires working with real time data. As opposed to data used to train AI models in a sterile lab environment, data in production is a moving target, and typically includes noisy, unstructured, or small data sets. Integrated technologies and AI expertise are key in achieving this kind of robustness and stability, that are the cornerstone of effective AI.
Predictive maintenance adoption challenges
Despite the huge potential in predictive maintenance, the adoption of this method is hindered by high barriers to entry. Launching an AI-based predictive maintenance solution requires a considerable investment to develop or buy and implement in the manufacturer’s real world environment, and adapt it to specific use case conditions.
Launching an AI solution in production is only half the battle, bringing on the challenge of maintaining the solution on the rails over time. Maintaining AI in production requires continuous control of data and model versions, optimization of human machine interaction, ongoing-monitoring of the robustness and generalization of the model, and constant noise detection and correlation checks. This ongoing maintenance of an AI solution in production can be an extremely challenging and expensive aspect of deploying AI solutions.
This combined challenge of launching an AI solution in a noisy, dynamic production environment and keeping it on the rails so it keeps delivering accurate predictions is the core reason that makes AI implementation profoundly complex. Whether trying to tackle this technological feat in house or turning to an external provider – companies struggle with AI implementation. Specifically, the main pitfalls along the digital transformation journey are in customizing AI solutions for specific environments, data, constraints and requirements; achieving stability and robustness in processing unstable and unstructured data; the operational effort in maintenance, updates, improvements and course-correction; and the adoption of new use cases towards a full AI transformation. The depth of these challenges is thoroughly demonstrated in Venturebeat’s research that reports that as many as 87% of AI projects in enterprises never even reach production stage.
Accelerate AI transformation with BeyondMinds
BeyondMinds has built the first enterprise AI platform that is universally applicable and easily adaptable across manufacturing use cases and environments. Every predictive maintenance solution built on the BeyondMinds platform is hyper-customized and production-ready. Since BeyondMinds delivers AI as a service (AIaaS), it enables manufacturers to scale their AI transformation and rapidly create ROI from AI. While many other AI solutions can take months or years to successfully reach production, BeyondMinds’ solutions enable companies to achieve production-grade AI within weeks. BeyondMinds’ mission is to create AI partnerships that enable the world’s most sophisticated companies to benefit quickly and enduringly from AI’s benefits, while freeing themselves of the risks and burdens of its development and ongoing maintenance.
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