Predictive Maintenance Software That Proactively Predicts & Prevents Equipment Failures
Predictive maintenance tools enable maintenance managers to predict equipment failures using real-time proactive data analytics and IoT sensors, limiting downtime & reducing emergency maintenance costs.
Which Data Points Would You Like for Your Predictive Maintenance Plan?
What Is Predictive Maintenance Software?
Predictive Maintenance Software is an application used by maintenance teams to deploy the maintenance strategy of predictive maintenance (PdM), which estimates and prevents equipment failure through real-time data analytics.
This proactive maintenance strategy aims to reduce unplanned downtime, prolong optimal asset life, and optimise spare parts for repairs.
By collecting raw data through condition-monitoring sensors and IoT devices, as well as using historical data, Predictive Maintenance Software enables maintenance teams to analyse patterns and correlations to process the current state of equipment and accurately predict failures.
Historic Data + Current Data = Intelligent Decisions
Both historical and current equipment data can be input into various machine learning (ML) models that automatically identify anomalies to make recommendations regarding failures and correct maintenance methods.
Ultimately, the combination of both ML and artificial intelligence is what sets predictive maintenance apart from other proactive strategies such as condition-based maintenance and preventive maintenance.
5 Most Recommended Predictive Maintenance Software Products
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
How Does Predictive Maintenance Work?
A predictive maintenance workflow consists of four stages:
- Collecting data
- Analysing data
- Setting condition indicators
- Training machine learning models
Predictive Maintenance Software works by collecting and analysing the historical and current data of equipment. Data is collected by condition-monitoring devices, such as vibration analysis sensors and pressure monitors, while equipment is running in real-time.
This data is then analysed as engineers aggregate data in a sophisticated platform, like a CMMS. From here, data is continuously analysed, shared, and actioned in machine-learning models.
Through automated data analysis, the predetermined failure periods of each asset are predicted. This enables maintenance teams to schedule maintenance in a more time and cost-effective way than that of a preventive maintenance strategy, which relies on a routine schedule for maintenance, repairs, and replacements.
Source: mathworks.com
How PdM Uses 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
Using a Digital Twin 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.
Examples of Predictive Maintenance Software Techniques
Data acquisition and set event indicators are possible through the use of IoT devices, namely condition-monitoring sensors. These include:
- Lubricant/Oil analysis
- Vibration analysis
- Infrared thermology (IR)
- Ultrasonic
- Current/Voltage analysis
- Acoustic/Pressure analysis
Find the Best Predictive Maintenance Software to Match Your Requirements
Which Data Points Would You Like for Your Predictive Maintenance Plan?
What Are the Benefits of Deploying Predictive Maintenance Software?
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.
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 advantages of PdM include:
- Minimises unplanned downtime of mission-critical assets
- Reduces time spent on maintenance planning by 20-50%
- Increase the life expectancy of machines and equipment by 20-40%
- Reduces unexpected equipment breakdowns and emergency repairs
- Minimises costs spent on labour, spare parts, and MRO inventory
- Improves safety throughout the workplace for technicians and operators
What Are the 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:
- Detailed and time-consuming planning to ensure PdM strategies are deployed throughout all facilities and include all relevant assets
- Purchasing the right condition-monitoring equipment can result in high upfront costs
- Hiring skilled staff or training maintenance teams can be expensive
Preventive Maintenance Software Vs Predictive Maintenance Software
How Does Preventive Maintenance and Predictive Maintenance Software Differ: 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.
Condition-Based Maintenance Vs. Predictive Maintenance
How Does Condition-Based Maintenance and Predictive Maintenance Differ: Although both strategies have a similar workflow in the way they collect, analyse, and action data based on the state of equipment, CBM, in a sense, 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 instinctively react by repairing or replacing that part.
6 Steps to Implementing a Predictive Maintenance Solution
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 instal 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.