Predictive Maintenance Tools | Key Benefits Explained

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    Predictive maintenance explained

    When compared to other types of maintenance like preventative where a maintenance team is attempting to prevent failure by performing routine maintenance on an even interval, breakdown where the maintenance is focused on letting the item fail before maintenance is performed, or reactive maintenance where there is an unexpected failure, predictive maintenance aims to predict failures.
    This is because preventative maintenance, the most prevalent type of maintenance, can cost a business a lot of time, effort, and money when things are not ready to have maintenance performed. That is why predictive maintenance was developed. The pioneers of predictive maintenance wanted to know if by using tools and data they could reduce maintenance costs even further than some other types of maintenance.
    Predictive maintenance has made a name for itself in different businesses like manufacturing plants, aerospace, and other lines of business as more and more of these industries are looking to a blended model between predictive and preventative maintenance for the equipment and assets.

    What is an example of predictive maintenance?

    Predictive maintenance, while it is many things, is the use of data and tools to predict failures.

    One of the major ways to implement predictive maintenance is to perform data analysis and data mining. Data is always a leading factor in solid maintenance work. So, when data is available, analyzed, and interpreted, it can lead to a predictive maintenance plan.

    So, let’s say that there is a crane that is used at your work and it was installed 5 years ago. There is a maintenance plan in place that inspects safety items every month, but the crane is still experiencing severe downtimes.

    If these downtimes and the number of jobs operated by the crane were recorded, a data analyst could in theory see how many jobs, or cycles, were performed by the crane before it experienced a downtime. This analysis could then be placed into a predictive maintenance plan where before the targeted job, or cycle, a specialized maintenance plan would be performed to keep the critical asset operating.

    Another way predictive maintenance can be performed is by performing object analysis. This comes in many forms like oil analysis, vibration analysis, leak detection, and many others. This utilizes sensors and the data the sensors provide to predict failures. These sensors may utilize artificial intelligence or machine learning.
    For this example, let’s imagine that you have a hydraulic motor for your work. This motor has maintenance performed on it to ensure it can continue to be used. However, hydraulic oil which is the lubricant and one of the main factors in the motor is an internal component that requires a certain level before performing maintenance.

    An analysis sensor may be installed to determine the quality of the oil inside of the machine. This helps to prevent early change out which costs the company additional money, or late change out which causes machinery damage.

    What are the types of predictive maintenance tools?

    So now that we’ve talked about some examples of predictive maintenance, let’s talk about some tools. What is out there and what do they offer?


    While this might seem obvious, but well-trained and capable personnel is one of the highest effective ways to start a predictive maintenance plan. Personnel with skills like data analysts will be able to provide data and reasoning to set up a maintenance plan.

    As well, personnel with skills like planners & schedulers will be able to take the data and plan & schedule maintenance with either internal or external partners. As well, personnel like While this is not a real-time solution and requires a lot of information to be provided to personnel, it is a great way to do predictive maintenance.

    Monitoring Equipment

    This can be a number of things like sensors, probes, thermal imaging, or ultrasonic acoustic microphones, but they rely on data, input, and computer programming to assess the health of the assets they monitor.

    These tools offer real-time data with consistent monitoring of the assets. As well, there are many tools like oil analysis and ultrasonic acoustic microphones that are built to do condition monitoring, which assess the condition of the oil are part they are installed in.

    Many of these utilize an Internet of Things, IoT, interface to notice changes in the environment and trigger maintenance before there is an issue. Equipment like this is supplementary to a maintenance program and maintenance strategy.

    A predictive maintenance program utilizes one, many, or all of these tools to reduce costs and ensure labor efforts are used based on analytics.

    Working with predictive maintenance tools

    Now that we know what is available, how can we work with predictive tools? While this might be specific to most businesses, but let’s take the example of the company with a hydraulic motor that they have installed a sensor.

    The first step they took to work with predictive maintenance tools is by installing the sensor in the hydraulic motor. This sensor monitors the condition and quality of the oil inside of the hydraulic motor and notifies the organization’s technicians when the equipment is entering low quality. If the organization has a Corrective Maintenance Management System or CMMS such as IBM Maximo, the sensor could create work to be assigned inside of the CMMS.

    However, often when a predictive maintenance tool is installed there is still preventative maintenance being performed. So, to next work with preventative maintenance, a predictive scheduler may be implemented. This is because you have now automated your equipment, so it makes sense to automate your schedule. This is because the scheduler, like IBM Maximo’s Graphical Scheduling application, will show how much work needs to be done, providing the ability to develop a workforce strategy.

    From there, more tools can be installed, such as a leak detector, to indicate when there is a leak of the fluid or a vibration sensor to detect anomalies in the motor. These would all stem from the original sensor and work towards increasing the ability of the predictive-based maintenance plan.
    Eventually, working with predictive tools will become more seamless thanks to a defined process and familiarity with technology.

    Need help improving uptime and efficiency?

    As industry 4.0 barrels towards more reliability and the ability to detect potential failures, predictive maintenance’s focus on efficiency and decreasing downtime has got you covered.

    When you’re attempting to prevent downtime and ensure your equipment is able to be used at the critical times you need it, a predictive maintenance strategy is built to keep your production running strong.

    This is because predictive maintenance uses techniques to predict failure before it happens. This stands out from preventive maintenance because preventive maintenance uses scheduled inspections to help keep equipment in working order. However, predictive maintenance looks to use equipment data and analysis to detect potential issues long before they occur. This allows a maintenance program to run more efficiently by reducing failure rates and decrease downtimes even if they are planned.

    Because predictive maintenance pushes organizations to use analytics, it naturally leads to strong solutions and cost savings. This ultimately leads to better performance from personnel and CMMS.

    5 Benefits from using predictive maintenance tools

    Time to find out what are the benefits advantages of predictive maintenance tools!


    Because of the nature of predictive maintenance, it requires a lot of data. This not only allows for organizations to work more with analytics in mind but also increases performance because the information is readily available to them.

    Flexible Approach

    Because this form of maintenance is built to be predictive, there are many techniques that can be used based on what you’re aiming to do. Want to monitor how an asset looks? Condition monitoring sensors can do that. If you’re looking to monitor damage? Vibration analysis can help with that. Want to detect potential downtimes? Want to record equipment heat? temperature and thermal analysis can do that.

    Reduced Labor

    Because the system performs maintenance based on machine learning of historical records, the system is only scheduling maintenance when it needs to be. This way machines are neither over-maintained nor under-maintained by technicians. A predictive maintenance program will keep labor costs lower than a preventive maintenance program.

    Increased Efficiancy

    One of the major benefits is IoT, which not only uses artificial intelligence or machine learning but can also record solutions. Many IoTs, such as IBMs IoT Watson, can store previous failures, fixes, and prevention methods.

    Equipment Assurance

    When working in something like manufacturing, the reliability of equipment and mitigation of downtime is key to profit. When assets are able to run without failure for extend periods of time, this maximizes profit and ensures production timing.

    Is predictive maintenance worth the investment?

    Now for the hard part, what are the disadvantages of predictive maintenance?


    As I’m sure you can imagine, things like advanced sensors that communicate with a CMMS and acoustic vibration analysis tools are not cheap. It can be very costly to get started and keep up with the necessary tools and development.


    Support for these tools can also be rather costly if you don’t have internal support. Monitoring an asset is helpful, but if when the monitoring technologies break and there is no support the savings is lost. Leading to costly service calls to fix the systems and costly loss in production timing.


    As with many maintenance programs, the first try may not be the right one. The detection point may need adjusting, the system process may need adjustment, or the performance might be subpar initially. This may lead to needing more technicians on a call than expected or a reassessment of the technologies.

    Is it right for me?

    To determine if predictive maintenance will impact your business is difficult, but if there is even one of your assets is identified as a potential candidate it is worth inspecting. Between flexible analysis techniques, an information-focused approach, and reduced labor cost, what is there to lose by looking?

    Predicting the future:

    Now that you have learned all about predictive maintenance and all its advantages and disadvantages, what are you going to do next?

    Are you going to do a risk analysis to see which of your equipment would benefit from this approach? Are you going to look into what type of technologies would suit your business? Or are you going to explore what systems or CMMS can handle what you’re looking for? Let us know!

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