Through condition-based monitoring and sophisticated machine learning, predictive maintenance (PdM) enables users to predict when an asset will fail before it happens. With fewer costs and a greater ROI, predictive maintenance strategies have become one of the most effective solutions for asset-heavy organisations.
The advancement in technologies, such as CMMS Software and IoT-enabled devices, has allowed businesses to implement maintenance plans that ensure increased reliability and availability of equipment.
With a market share of £6.3 billion, predictive maintenance solutions provide a variety of benefits for all types of industries and organisations. From reducing unplanned downtime to extending the life expectancy of mission-critical assets.
What Is Predictive Maintenance?
Predictive Maintenance is a form of maintenance that tracks and monitors the condition and performance of equipment during normal operation. By doing so, maintenance managers and technicians can identify possible defects and fix them. This results in a reduced likelihood of equipment failure, leading to unplanned downtime.
Similar to Preventive Maintenance (PM), predictive maintenance is a proactive strategy that aims to eliminate breakdowns in equipment and machinery. But, the difference is, PdM can predict when equipment will fail with the use of devices and sensors. This allows maintenance frequency to be as low as possible to avoid a costly reactive strategy.
Like most proactive maintenance approaches, predictive maintenance provides the tools to:
- Reduce unplanned downtime of equipment that is critical for production
- Minimise the time and money spent on repairs and maintenance
- Ensure assets are available at all times and in optimal working condition
- Prolong the life expectancy of assets to reduce high turnover rates that can be costly to a business’s bottom-line
How Does Predictive Maintenance Work?
Predictive maintenance uses condition-based monitoring to continuously track an asset’s performance in real-time, while in operation. It can be deployed to monitor most assets from large in-field infrastructure to equipment and machinery.
To effectively collect the right data, predictive maintenance programs have to be paired with the right condition-monitoring technology. In particular, a CMMS and multiple IoT-enabled devices.
With over 10 billion connected devices, the Internet of Things (IoT) plays a key role in the process of forming an efficient predictive maintenance strategy. By collecting the necessary data from condition-monitoring sensors, IoT devices can take that data and connect it to a maintenance management system.
Source: IHS, globalsources.com
From there, data is continuously analysed, shared, and actioned through machine-learning technology. Eventually taking Machine-to-Machine (M2M) technology to the next level.
The information captured by predictive maintenance systems and IoT sensors can vary depending on your equipment and machines. Examples include:
Through the use of Infrared (IR) cameras, technicians can detect high temperatures (hot spots) for worn components such as electrical wiring.
Seen as a cheaper alternative to ultrasonic imagery, acoustic analysis helps to detect liquid, gas, and vacuum leaks.
Sensors can be used to determine an increase or decrease in vibration speed of essential components such as pumps and compressors.
Allows engineers to constantly check the condition of a machine’s oil lubricant and determine if it has been compromised by other particles and contaminants.
What Are the Advantages of Predictive Maintenance?
The process of efficiently monitoring and optimising the performance of equipment is critical for businesses that rely on their assets to generate revenue. With the use of a CMMS solution and various IoT devices, this is achievable with a predictive maintenance strategy. The advantages of PdM include:
- Minimises unplanned downtime of mission-critical assets
- Reduces time spent on maintenance
- Increase the life expectancy of machines and equipment, in some cases by 20-40%
- Reduces machine breakdowns and unexpected failures
- Minimises costs spent on labour, spare parts, and equipment
- Reduces stock of spare parts due to increased service life of assets
- Improves safety throughout the workplace for technicians and operators
What Are the Disadvantages of Predictive Maintenance?
Although predictive maintenance enables asset-heavy organisations to achieve an increase in asset uptime and a reduction in maintenance costs, there are some disadvantages that can deter businesses away from this strategy. Disadvantages of predictive maintenance include:
- Detailed and time-consuming planning to ensure this maintenance approach is deployed throughout each facility and details all assets
- Purchasing the right condition-monitoring equipment which can result in high upfront costs
- Hiring skilled staff or training maintenance teams which can be expensive
A Step-By-Step Guide to Implementing a Predictive Maintenance Plan
From identifying priority assets to connecting your IoT devices with an effective CMMS, making sure you correctly deploy predictive maintenance is critical to achieving a good ROI. After securing stake-holder buy-in, calculating your budget, setting your KPIs (Key Performance Indicators), and deciding on the type of software you need (ranging from cloud-based and mobile to on-premise), the implementation process can begin.
Step 1: Identify Priority Assets
To achieve an accurate understanding of your ROI with predictive maintenance, you’ll first need to identify the assets that are critical to your operations. By looking at previous breakdown records and RCA (Root Cause Analysis) reports, you’re also able to highlight the equipment with the highest repair costs.
Step 2: Start Training Staff
The use of new and advanced tools that PdM requires means your maintenance team will need to be trained. Not only does this mean making sure operators know how to identify maintenance alerts, but it also means training your technicians and engineers on how to maintain and repair IoT tools.
Step 3: Set Condition Baselines
A key part of deploying predictive maintenance is to define your maintenance baselines. With a preventive maintenance strategy, a target could be to service a machine after 10,000 hours of use. Whereas with a PdM approach, your baselines would involve conditions and performances in real-time. For example, if a machine is producing more noise than the baseline decibels you have set, maintenance would need to be performed right away.
Step 4: Install IoT Devices and Sensors
Once you’ve identified the IoT devices and sensors that you require to meet your set baselines, it’s time to instal them. This could be a vibration meter, an oil measurement, or a thermal imagery camera.
Step 5: Connect Devices to a CMMS
The next step is to connect your IoT devices and sensors to an effective CMMS tool. This allows you to monitor asset data in real-time as well as collect, analyse, and store critical information.
Step 6: Schedule Maintenance
Once your predictive maintenance program is in place, it’s time to execute it. An efficient way to begin your plan is to run a pilot test on just one or two of your most important assets. This helps you to gain an understanding of how data will be collected and to iron out any issues. As you begin to collect data, you can then start to analyse asset performance and monitor machine conditions in real-time.
What’s the Difference Between Preventive and Predictive Maintenance?
Although both preventive and predictive maintenance strategies fall under the umbrella of proactive maintenance, they are two separate approaches that can appeal to different organisations and industries.
Whereas PdM basis maintenance on real-time conditions and performance, preventive maintenance sticks to a scheduled strategy. PM schedules maintenance based on triggers such as usage and time. For example, a vehicle may be serviced only once it has reached 10,000 miles. Although this approach requires less capital investment than a PdM strategy, performing repairs on assets whether they need it or not can lead to a risk of excessive preventive maintenance.
Through trigger-based strategies, preventative maintenance allows you to determine the average life cycle of each asset. As opposed to monitoring assets with predetermined conditions and real-time performance like predictive maintenance. However, a PdM strategy will also require a large investment in maintenance teams, training, and equipment.
Other Types of Maintenance Strategies to Consider
Although the benefits of predictive maintenance include increased productivity and reduced maintenance costs, it is only the third most deployed strategy with 51% of maintenance personnel favouring it.
Other types of maintenance include:
Reactive Maintenance (also Breakdown or Corrective Maintenance) is the process of repairing equipment only when it fails. Although this strategy can result in costly unplanned downtime, a delay to production, and excessive breakdown costs – it is still favoured by 57% of maintenance personnel.
Preventive/Preventative Maintenance (PM)
Preferred by 80% of maintenance staff, Preventive Maintenance/Preventative Maintenance (PM) is the process of routinely scheduling maintenance, repairs, and services to ensure mission-critical assets are kept in optimal working condition. As well as collecting data to understand an asset’s life cycle and reduce equipment turnover, there is a high risk of excessive preventive maintenance which can be costly in the long run.