Best Predictive Maintenance Software
Use our sophisticated finder tool to identify Predictive Maintenance Software that meets your industrial maintenance needs and prevents asset failures.
Which Data Points Would You Like for Your Predictive Maintenance Plan?
What Is Predictive Maintenance & How Does It Work?
Predictive maintenance (PdM) is a technique centred around diagnostic and prognostic models. It enables organisations to collect large amounts of raw data on equipment health and performance using IoT devices and sensors.
This data is analysed for patterns, anomalies, and correlations to predict and avoid equipment failure at the most accurate point possible. With the aim to ease fundamental pain points of industrial and plant maintenance; costs, downtime, and longevity.
At its core, predictive maintenance feeds on continuous data collection, measuring variables such as vibration, frequency, and temperature. With this data, maintenance leaders can identify what type of maintenance needs to be carried out and when. Before training machine learning algorithms to understand performance variables.
Predictive maintenance involves continuous trial and error by figuring out the optimal performance of equipment and understanding when it is underperforming or defective. Essentially, it works by:
- Affixing condition and performing monitoring sensors to equipment
- Collecting and sorting that data into measurable variables
- Setting parameters for those variables
- Being alerted when parameters are missed or anomalies occur
- Understanding what maintenance is required to correct that anomaly or defect
- Training machine learning algorithms to detect similar anomalous and automatically schedule future maintenance
Advancements in Industry 4.0, big data, IoT, machine learning, and artificial intelligence have spearheaded the growth of predictive maintenance. So much so that it is the pinnacle of proactive maintenance techniques. It has expanded beyond traditional preventive and condition-based maintenance strategies.
Plant and industrial maintenance practices have also flourished due to the influx and availability of data. Coupled with the demand for greater efficiency, availability, and cost-cutting, it is no surprise the adoption rate and market share of PdM are expected to grow three-fold by 2028.
To leverage large amounts of data, analyse it, and effectively implement it within existing maintenance workflows, a digital solution is required. And that’s where Predictive Maintenance Software comes in.
Why Organisations Implement Predictive Maintenance Software
Predictive Maintenance Software is an application deployed by maintenance teams to help collect, store, and analyse data that is then used to train ML algorithms and promote predictive maintenance techniques.
PdM Software is implemented to deliver on six key objectives:
- Reduce unexpected, non-budgeted maintenance and repair costs
- Eliminate unplanned downtime of mission-critical assets
- Increase equipment reliability and availability with estimated planned downtime
- Predict and prolong an equipment’s remaining useful life (RUL)
- Increase maintenance productivity
- Ensure proactive maintenance is only carried out when necessary
These objectives are achieved with PdM Software features like advanced analytics, spare parts and MRO management, work order management, and maintenance scheduling.
A PWC study showed that organisations implementing predictive maintenance saw a decrease in unplanned downtime (91%) and repair costs (12%), alongside an increase in asset availability (9%) and longevity (20%). Some studies also showed a 250% ROI for deploying Predictive Maintenance Software when compared to other maintenance techniques.
Source: mathworks.com
Costs vs ROI Potential
Return on investment is a crucial factor for almost any purchasing decision, especially a solution that may have upfront, ongoing, and sensor-integration costs such as Predictive Maintenance Software.
On average, a predictive maintenance solution will cost between £500 and £5,500 per year. But there are other costs to factor in like:
- IoT devices and sensors
- Employee training
- Data migration
- Data security and storage
- IT infrastructure and warehouse space (for on-premise solutions)
Although the costs seem to pile up, consider the costs of unplanned downtime. In plant and industrial industries alone, it ranges between £35,000 and £2 million per hour.
The ROI potential of Predictive Maintenance Software is huge, particularly for manufacturing and automotive industries. Not just financially, but in terms of efficiency and productivity. However, this hinges on successful implementation including software integration, the use of sensors and IoT devices, deploying ML algorithms, and training employees.
Our expert’s view on predictive maintenance
“I think [predicitve maintenance systems are] the future, it’s less engineers deciding when to do calendar-based maintenance and more analysis on failure codes, failure modes, and trends across the industry to enable predictive maintenance, which is what every operator wants. They want to predict when they need to do maintenance rather than failures in service.”
| Roy Milne, Founder at Asset One |
5 Leading Benefits of Deploying a Predictive Maintenance Strategy
91% of manufacturers who deployed a predictive maintenance program saw a reduction in repair time. As well as a 9% increase in equipment uptime and a 20% extension in the life cycle of ageing assets.
The five most lucrative benefits of PdM include:
- Reducing time spent on maintenance planning by 20-50%
- Increasing the life expectancy of machines and equipment by 20-40%
- Reducing unexpected equipment breakdowns and emergency repairs
- Minimising costs spent on labour, spare parts, and MRO inventory
- Improving safety throughout the workplace for technicians and operators
Predictive Maintenance Software is most beneficial when used in industries that rely on the output of heavy machinery and equipment. That includes oil and gas, infrastructure, agriculture, construction, and manufacturing.
Types of Predictive Maintenance Techniques
Oil Analysis
Oil condition monitoring is performed using oil quality sensors or fluid monitoring sensors to identify foreign objects that are a byproduct of overheating or erosion. As well as show the presence of water and perform particle counts. If the analysis alerts to any change in the baseline sample, maintenance is scheduled to drain and replace the oil.
Vibration Analysis
Predictive Maintenance Software can be set up to determine anomalies in the vibration patterns of rotating equipment. For instance, if a vibration monitoring sensor indicates a spike during the operation of a CNC lathe, it is likely there is a misalignment or a bearing is worn down.
Infrared (IR) Thermology Analysis
With the use of infrared cameras, temperature maps and hot spot parameters can be put in place to identify defects. This form of non-destructive testing checks for anomalies that are not visible to the human eye, like electronic malfunctions or overheating due to low lubrication levels.
Acoustic & Ultrasonic Analysis
Ultrasonic and acoustic analysis is a form of predictive maintenance that detects unusual pitches and sounds unheard by human ears. Spikes in sound frequency levels can determine if a rotating machine is deteriorating or if there is an oil leak.
What Are Predictive Maintenance Tools?
To ensure that the data being collected in your Predictive Maintenance Software is accurate, you’ll need to use a variety of condition-monitoring tools. These tools collect real-time data and provide the best chance possible for your PdM strategy to succeed.
Such predictive maintenance tools include:
- Vibration monitoring sensors
- Gas pressure gauges
- Humidity and temperature sensors
- Fluid monitoring sensors
- Ultrasonic microphones for sound analysis
- Motor circuit analysis (MCA) tools
- IR cameras
UK’s Best Predictive Maintenance Software Solutions
MaintMaster
The Product
A self-configurable Predictive Maintenance Software with fixed implementation, training and support costs.
Ideal For
Organisations looking for a low TCO, self-configurable CMMS with out-of-the-box compliance capabilities
Industry Fit
Manufacturing, Engineering, Construction, Energy, and Utilities
Pricing
From £12 per user, per month
MaintainX
The Product
A self-service maintenance management solution with a library of customisable processes and integrated messaging.
Ideal For
Organisations looking for a self-service maintenance management solution that can be easily scaled
Industry Fit
Manufacturing, Food & Beverages, Hospitality, and Education
Pricing
From £13 per user, per month
ShireSystem
The Product
A modular maintenance management solution that replaces manual H&S, asset and maintenance processes.
Ideal For
Organisations with a total first-year budget of at least £3,000
Industry Fit
Manufacturing, Engineering, Construction, Automotive, and Food & Beverages
Pricing
From £744 per annum
Workmate by Cayman
The Product
A quick-to-implement, all-in-one CMMS solution with flexible deployment.
Ideal For
Organisations with 3+ maintenance engineers
Industry Fit
Manufacturing, Engineering, Construction, Logistics, and Food & Beverages
Pricing
From £1,490
Fiix
The Product
A CMMS solution that connects rapidly to production and business systems and provides AI-driven insights.
Ideal For
Organisations in equipment-intensive, reliability-focused industries
Industry Fit
Manufacturing, Engineering, Construction, Logistics, Utilities, Oil & Gas, and Retail
Pricing
From £40 per user, per month
6 Crucial Steps For Implementing Predictive Maintenance Software
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 Condition-Monitoring Devices & Sensors
Once you’ve identified the condition-monitoring devices and sensors that you require to meet your set baselines, it’s time to install them. This could be a vibration meter, an oil measurement, or a thermal imagery camera.
Step 5: Connect Devices to a Predictive Maintenance Software System
The next step is to connect your IoT devices and sensors to a predictive maintenance solution. This allows you to monitor asset data in real-time as well as collect, analyse, and store critical information.
Step 6: Initiate Your PdM Strategy
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 through machine learning models.
Find Maintenance Management Software For Your Predictive Maintenance Tasks
Which Data Points Would You Like for Your Predictive Maintenance Plan?
What Are the Risks and Challenges of Predictive Maintenance?
Although predictive maintenance enables asset-heavy organisations to achieve better asset uptime and reduce costs associated with maintenance planning, there are some disadvantages that can deter businesses away from a PdM strategy. Disadvantages of predictive maintenance include:
- Implementation & tool costs: Predictive maintenance isn’t cheap. There’s the cost of software implementation, purchasing sensors, installing sensors, and training employees. But, as shown, the potential ROI is substantial.
- Time: You need to acknowledge that everything takes time when it comes to predictive maintenance. The implementation of sensors and devices, setting optimal condition-based parameters, training ML algorithms, supporting staff, and collecting data – it all takes time.
- The accuracy of data: Data is king with PdM. If a sensor is misaligned or installed incorrectly, you could be hinging a lot of maintenance efforts on inaccurate data. This can potentially have a disastrous impact on equipment health and your bottom line.
- PdM is complex and hard to execute: Predictive maintenance is a technique specifically reliant on data collection and analysis. For those with little experience in data handling, this adjustment can be tough. This is a major factor why 85% of UK businesses see predictive techniques as a major challenge.
- Adapting to new ways: Whether switching from spreadsheets or a legacy CMMS, predictive maintenance is a completely refined way of performing maintenance management. This can make it seem alien to some employees, as training will need to be implemented to ensure old habits don’t creep back in.
- Hiring and training employees: Predictive maintenance requires significant knowledge in data analytics and management. Without staff skilled in machine learning or AI processes, you may need to hire experts or outsource, which can be expensive.
How Predictive Maintenance Compares to Other Maintenance Strategies
Preventive Maintenance
Whereas both maintenance plans are proactive, methods like preventive maintenance and Total Productive Maintenance are more costly and time-consuming. Instead of using ML to predict when assets will fail, businesses will use historical data to create a periodic maintenance schedule. By doing so, parts and equipment that are still usable are instead swapped out and replaced earlier than initially needed.
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Condition-based Maintenance
Although both strategies have a similar workflow in the way they collect, analyse, and action data based on the state of equipment, condition-based maintenance is more reactive. Instead of predicting when equipment will fail and structuring a maintenance plan around that period – like PdM, machine operators will be alerted to when a set event is triggered and react by repairing or replacing that part.
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Reactive Maintenance
Reactive maintenance is the complete opposite of a planned and proactive strategy. It is a technique used to save money on preventive measures by fixing equipment when it fails. Only then will repairs and maintenance take place.
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Frequently Asked Questions
What Is the Cost of Predictive Maintenance Software?
For cloud-based Predictive Maintenance Software, the prices vary depending on the number of users required, the number of work orders, and the amount of equipment. As an example:
- For smaller operations with fewer requirements, the average cost starts at £500 per annum
- For mid-sized operations with up to 50 users, the average price is between £5,000 and £7,000 per annum
- For large organisations with over 100 users, the average price is between £20,000 and £30,000 per annum
The cost of a cloud-based PdM solution can fluctuate based on implementation costs, costs for purchasing and installing sensors and IoT devices, employee training, and data migration.
For on-premise solutions, costs can run much higher. This is because of the IT infrastructure required, the space to store servers and data centres, and the one-off cost of a perpetual license.
What Are the Deployment Options For Predictive Maintenance Software?
Although some specialist hybrid cloud models are available, there are essentially two forms of Predictive Maintenance Software deployment; cloud and on-premise.
Cloud-based predictive maintenance solutions (or SaaS) are based on a subscription model, whereby you rent server space from a vendor and pay to use their product. Although customisation, offline access, and data control may be a downside, cloud-based PdM solutions have several advantages:
- There are minimal – if any – upfront starting costs
- They’re quick and easy to set up with an average implementation time of 0-3 months
- Data is automatically backed up and encrypted for security
- Vendors provide access to 24/7 support and training resources
- A subscription model makes the cost more manageable and budget-friendly
An on-premise predictive maintenance product is stored locally, both on in-house servers and within the boundaries of your premises (hint the name, on-premise). Although upfront and ongoing costs are high, and implementation can take up to two years depending on the scale of your operations, it does have some advantages:
- You’re in complete control of your data, security, and infrastructure
- A dedicated IT team is specifically focused on improving and updating the system
- You’ll have offline access to all data when in and out of the office
- The system is yours to customise how you see fit, ensuring it matches your requirements and making it unique to your operations
Which Industries Benefit Most From a Predictive Maintenance Solution?
Due to the high-level focus on data and analytics, predictive maintenance is a technique often associated with large organisations that rely on the upkeep and production of heavy equipment and machinery. In this case, Predictive Maintenance Software will appeal most to operations in:
- Manufacturing
- Automotive
- Construction
- Engineering
- Energy & Utilities
- Farming & agriculture
- Food and beverage manufacturing
How Does Predictive Maintenance Use Machine Learning Models?
ML models can be trained to work with specific datasets and condition indicators, which automate the process of predicting when equipment failure will occur. They can:
- Detect anomalies in data readouts
- Identify exact faults in equipment
- Predict the optimal life and subsequent failure period of an asset
Can a Digital Twin Be Used In Predictive Maintenance?
The use of a digital twin can help to improve the efficiency of data related to various predictive maintenance scenarios. By creating a digital twin to reflect a physical object, maintenance teams can run numerous simulations with real-time data collected via sensors and other IIoT equipment on a virtual model.
This helps to understand how an object performs in changing environments and allows businesses to study improvements while having a greater understanding of what to do with a product once it reaches its end of life.