Predictive Maintenance Best Practices

Predictive maintenance with words written around it that are predictive maintenance best practices.

Moving to a predictive maintenance (PdM) approach can appear complex and costly, but it does not have to be. A step-by-step strategy combined with the correct software solution can make this a simple procedure that yields tremendous rewards. However, it is critical that you do not seek to transform your whole maintenance model into a predictive maintenance operation all at once.

If you’re just starting, running a pilot focused on one department or work cell, especially one that consists of business-critical machinery and equipment, is crucial. Then, based on the initial trial results, you will be able to adjust your strategy and expand your PdM program.

Today we will discuss the recommended predictive maintenance best practices to follow during the transition. We’ll also highlight LLumin’s powerful and unique computerized maintenance management system (CMMS+) software and the superior support provided to customers throughout the entire PdM implementation process.

Predictive Maintenance Best Practices

Predictive maintenance solutions are well-known for lowering equipment maintenance costs while increasing equipment dependability and maximizing uptime. However, building an effective PdM program will include an initial investment in the process, including training, continual review, and data monitoring.

As industry 4.0 technology advances, more organizations in all industries are expected to adopt a smart and proactive approach to maintenance. If you want to implement a predictive maintenance model, we’ve prepared a list of best practices to get you started. 

These predictive maintenance best practices include:

  • Assemble your support team
  • Determine critical machinery and equipment
  • Enable connectivity to machines and associated parameters through communications to existing control systems or Internet of things (IoT) sensors
  • Implement a CMMS system with artificial intelligence (AI) and machine learning (ML)
  • Provide relevant training
  • Start with a small pilot
  • Analyze and review data

We will discuss each below.

Assemble Your Support Team

Choosing the wrong team to conduct PdM testing and analysis will lead to poor results when establishing a PdM maintenance model. It’s a good idea to assemble a cross-functional team of specialists and top management professionals when building the support team for your PdM program.

If you lack trained personnel to support program implementation, you should consider working with an outside vendor that can guide your team through the PdM implementation process, providing expertise and helping you accomplish your company’s goals and objectives. There are consulting firms that provide high levels of expertise. Some CMMS software providers may also have the expertise and skill set to provide guidance.

When working with LLumin, your PdM implementation will be configured to match your unique company goals and business processes, and a dedicated project manager will help you throughout the entire implementation process.

Determine Critical Machinery and Equipment

The first step in every PdM program is to create a list of critical machinery and equipment. Start by determining the assets that have suffered the most substantial losses. Then, when creating your database, prioritize those assets to guarantee that critical asset maintenance requirements are addressed when your new system is launched.

Enable Connectivity to Machines

This stage involves either leveraging and connecting to existing control systems or machine intelligence or purchasing and installing appropriate IoT-enabled sensors and systems where required, allowing you to monitor important conditions, parameters, and alarm codes. Example data points could be: 

  • Throughput speed
  • Output quality
  • Power use 
  • Idle times
  • Vibration
  • Wear and tear 

Data collection will be specific to the critical assets and equipment you choose to monitor.

Implement a CMMS System With AI and ML

Implementing a CMMS with cloud framework, AI, and ML capabilities will allow you to collect data remotely and build a highly-accurate predictive maintenance model. 

A comprehensive software platform, like LLumin’s CMMS+ software, utilizes cutting-edge machine learning, powerful algorithms, and analytics to provide solutions to problems before they occur. LLumin’s CMMS+ software minimizes machine downtime, enabling operations and production to remain consistent over time.

It is important to keep in mind that your chosen CMMS system will be at the heart of your PdM implementation process. This platform will be essential in the real-time monitoring of critical machinery and equipment, recommending maintenance procedures, and providing valuable insight into large volumes of collected IoT sensor data.

Provide Relevant Training

When implementing a PdM model, it is critical to provide relevant training to support staff and individuals who will be working closely with your CMMS software and critical assets. It is a necessary process and will help reduce the likelihood of a poorly-implemented program that results in insufficient response times and maintenance.

Often, many people involved in the process, including facility maintenance technicians, receive little or no training beyond what is initially provided by their vendor system. Unfortunately, depriving support staff of necessary training can directly impact the effectiveness and success of a new PdM program.

When you work with LLumin, our expert staff will train your team to use and feel comfortable navigating the software and all needed functionality. We provide superior customer service to our customers throughout the entire PdM implementation process. After implementation, our experts remain available to assist with any new questions or issues that arise.

Start With a Small Pilot

Your initial pilot should focus on a small number of critical assets using IoT sensors linked to your CMMS software. Depending on your assets, this process should take several weeks and will collect real-time or scheduled data.

You will be able to better analyze how certain parts and components of a machine or equipment contribute to overall performance by combining historical data and data collected through IoT sensors (conditions-based monitoring). This will help you understand the true costs of machine downtime and how it affects business operations, resulting in better business decisions.

The analysis process involved in your first pilot will enable you to take action and monitor the results. If your initial pilot succeeds, you can expand your PdM model to include more (or all) of your machines and assets.

Review and Monitor Data

Finally, monitor your initial pilot assets over time before implementing your larger PdM program. The data you collect will become the foundation as the full implementation is rolled out. This process will involve establishing set goals and critical KPIs to determine performance levels. 

It is critical to understand your current performance levels and scores for any KPIs you wish to improve. You’ll need to know where your KPIs are before you get started so you can measure after-implementation results and have hard numbers to compare against.

Unidentified equipment failures and unscheduled downtime can result from programs that do not review and monitor data regularly. To ensure the overall success of your program, you must support and encourage support staff to do their jobs diligently.

This process can take several weeks, so be patient. After collecting enough failure data, you will be able to generate better predictions and optimize your alarm thresholds.

Implement Predictive Maintenance Best Practices With LLumin

If you want to implement a PdM model, you’ll need advanced maintenance software. LLumin’s CMMS+ works with any machine’s sensors or control system to track and analyze the status of assets. Our cloud and mobile-ready software streamlines asset management and all logistics and activities required to optimize performance levels.  Including scheduling, monitoring, and managing personnel, supplies and tooling, needed supply chain partners, and any other resources needed for successful maintenance management. CMMS+ also provides powerful but nimble dashboards and visualizations that enable remote asset monitoring and visibility to all asset conditions and statuses, along with potential areas of operations risk.

LLumin’s easy-to-use CMMS+ predictive maintenance software combines data from machine sensors with condition-based workflows to execute immediate responses and actions for optimal asset management. 

LLumin provides a honed implementation process to execute and ensure effective use of the CMMS+ software. What’s more, working with LLumin does not end with the CMMS+ go-live. We provide ongoing training and support to our customers so that they can adapt to and perform well in changing market conditions. If you are looking for a cutting-edge CMMS+ accompanied by a seamless and rapid implementation process, then LLumin is a perfect fit.

Getting Started With LLumin

LLumin develops innovative CMMS software to manage and track assets for industrial plants, municipalities, utilities, fleets, and facilities. If you’d like to learn more about predictive maintenance best practices, we encourage you to schedule a free demo or contact the experts at LLumin to see how our CMMS+ software can help you reach your efficiency and cost-cutting goals.