Maximize Uptime and Achieve Operational Efficiency with Optimized Predictive Maintenance

Understand Predictive Maintenance (PdM) Benefits

Using AI for a predictive maintenance strategy can help your factory avoid costly issues resulting from equipment failure and unplanned downtime, including lost productivity, repair costs, and missed customer delivery times and expectations.

Predictive maintenance provides a cost-effective alternative to the expensive run-to-failure mentality prevalent at companies using older legacy systems. It also improves upon outdated, labor-intensive preventative maintenance approaches, in which maintenance is performed in rigid, fixed increments.

A data-driven approach, by contrast, lets you intervene before costly downtime occurs. Through modern software-defined plant floor infrastructure, operational data is collected from multiple failure points on physical equipment throughout the factory—often production equipment, engines, and other high-value assets.

Car manufacturers, for example, can use sensor data to monitor the condition of welding guns, which are prone to overheating. Analytics are used to forecast future failures based on the machine’s working condition, triggering alerts as thresholds are approached.

With an AI-enabled predictive maintenance strategy, your organization can: 

• Maximize uptime by adopting a predictive approach. Sensor data provides advance notice of potential failures, driving more effective decision-making and expediting repairs. You can plan ahead to minimize the impact of machine failure on operations, schedule maintenance proactively to avoid lengthy disruptions to production, and divert loads to other equipment while machines are being serviced.

Now that you know some of the benefits of intelligent detection in the factory, you are ready to start planning your implementation. Here are the steps you should take to coordinate a successful predictive maintenance deployment.

Evaluate Your Risk Level

The first step is to assess your organization’s risk level. Manufacturers are exposed to myriad risks that predictive maintenance and AI-enabled machine condition monitoring help mitigate, from equipment failure and defects to safety and compliance. Compiling a report on potential risks will allow you to determine if an AI solution is worth the upfront investment.

• Internal risk assessment should be conducted to gain insight into your operational risks. Some important data points to consider include:
  • Hazard analyses
  • Machine downtime logs
  • Product quality surveys
  • Safety incident reviews
  • Records and historical data on process deviation
  • Defect rates
  • Compliance issues
  • ISO certification statuses
• External risk assessment and research into the financial implications of unexpected downtime should also be conducted. Research how production stoppage can result in supply-chain bottlenecks and cause commodity price fluctuations, and conduct surveys on how downtime impacts customer satisfaction. AI solutions can help mitigate your business risks by keeping operations running smoothly.
  
  The specific risks your organization is exposed to will vary by subsector:
• • Discrete manufacturers, such as automotive, appliance, and electronics makers, are more susceptible to issues with quality loss and supply chain disruptions due to machine failure.
  • Process manufacturers, including pharmaceutical and food and beverage companies, are more vulnerable to problems pertaining to formula errors, regulatory compliance, and process control.

You should also rely on external research, including competitive analysis and industry reports, to compare digital transformations in plants or factories similar to your own. For example, you can read a success story on how BMW used Industry 4.0 solutions to automate and enhance critical quality control processes in their factories.

Partner with an AI Solution Provider

Once you are ready to move forward, consider bringing in outside help to do the heavy lifting. Working with a technology partner simplifies the entire process from planning to implementation, helping to save time, reduce costs, limit complexity, and overcome implementation hurdles. Intel collaborates with a worldwide network of AI and industrial solution providers and systems integrators (SIs) to ensure that partner solutions are optimized for Intel® hardware and that Intel-based systems are designed for high performance and smooth operations.

Prescient Technologies, for instance, provides a flexible digital twin solution that lets you see the impact of your data in thirty days. Built upon Intel® Edge Insights for Industrial platform and the OpenVINO™ toolkit, it helps your operators quickly power through scattered operational data sources to deliver clear visual and actionable data insights. Contact them here.

Read recent case studies on how Intel's ecosystem partners implement Industry 4.0 solutions across the manufacturing industry.

Implement Your AI Solution

The next step is to deploy AI capabilities throughout your factory so that you can monitor your equipment. This requires both physically installing AI-capable technology to collect data on each piece of equipment and deploying an AI algorithm to analyze collected data in real time to identify and prevent maintenance, compliance, and productivity-related issues.

Here’s a more detailed summary of what is involved in deploying an AI solution in your factory:

1. Install AI-capable technology in equipment to collect operational data and failure history and define machine-specific characteristics. Operational data provides the basis for predictive maintenance, specifically a machine’s normal functioning data and error data. Sensors are often used to collect this data. For example, if a hydraulic pump is being monitored, sensors can capture vibration rates, oil pressure, speed of fluid, and other relevant parameters. Alternatively, if the machine is part of a software-defined control (SDC) system, telemetry agents are installed to capture the machine’s characteristics in real time.
2. Prepare your machine data at the edge or in the cloud. A database is used to store the raw data for analysis. A data scientist will preprocess the data to transform it into a suitable format for the algorithm. The preprocessing step improves data accuracy and allows the algorithm to process the data efficiently.
3. Train your AI algorithm on preprocessed data to create a model specific to the machine’s operational data. A data scientist will identify the appropriate algorithm to use based on the nature of the data and key performance indicators such as latency, the size of the model, and accuracy.
4. Deploy trained AI models on edge devices or on a central server that collects streaming data from various machines to make predictions collectively. The best option for your organization will depend on your aforementioned risk level. Operations that require real-time predictions should deploy the model on edge devices, while those with more leeway in predicting failures can use a central server.
   
   However, there are tradeoffs to consider. Reading data in real time on edge devices may necessitate using a smaller model, which could be less accurate than a large model, leading to variations in the forecasts.
   
   Edge devices with AI acceleration are recommended for edge use cases. OpenVINO™ toolkit, an open-source toolkit for optimizing and deploying AI models, allows you to run AI applications at the edge with improved efficiency.
   
   Additionally, Intel® AI tools powered by oneAPI help you accelerate machine learning workloads on Intel® architecture with optimized packages for popular frameworks and libraries, including PyTorch, Modin, scikit-learn, XGBoost, and others.

Select and Deploy Intel® Solutions

Utilizing the right combination of technologies is crucial to your successful AI deployment. With a robust end-to-end AI platform, Intel provides the components you need to implement your Industrial AI solution, including:

• Hardware solutions, such as AI-ready processors with features like Time-Sensitive Networking (TSN) for low latency and ruggedized, heat-resistant hardware for industrial conditions¹
• Software solutions, such as industrial-focused development platforms
• Partners who offer ready-to-deploy, custom solutions or system integration expertise to help you deploy Intel-based solutions and minimize system complexity

Some of the Intel offerings available to help fuel your predictive maintenance solutions include:

• Intel® Xeon® Scalable processors deliver the performance to drive advanced analytics on the factory floor. These processors contain built-in Intel® AI Engines for accelerating AI workloads at scale, delivering fast insights into operational data by boosting the performance of deep-learning training and inferencing tasks on CPU cores.²
• The Intel® Edge Insights for Industrial platform provides the foundation for harnessing factory data to improve operations. With support for video and time series data ingestion, this open, ready-to-deploy software package comes with prevalidated software components to accelerate industrial AI deployments. It includes AI analysis, can publish to local applications or the cloud, and provides the flexibility for customized solutions.
• Intel® IoT Market Ready Solutions (Intel® IMRS) from Intel's ecosystem partners integrate Intel® hardware and software into ready-to-deploy industrial solutions that negate downtime risk, unlock operational efficiency, and improve worker safety. These ready-to-deploy, Industry 4.0 solutions are optimized for running AI applications on Intel® processors, enhancing performance at the edge and on-premises.

Get Started on Optimizing Predictive Maintenance

A predictive maintenance and AI-based machine condition monitoring solution lets your organization maximize uptime to achieve operational efficiency. Intel® hardware, software, and our partner network can help you deploy a market-ready AI solution and realize your ideal operational end state. Get started by connecting with your Intel representative or an Intel® technology partner and put AI to work for your organization today.

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