Predictive Maintenance Tools - 7 Types to Check Out

In today’s business landscape, predictive maintenance has emerged as a critical strategy to optimize equipment performance, reduce downtime, and minimize maintenance costs. In this article you will learn about some tools that can be used to simplify the complexity involved with implementing a successful predictive maintenance program.

What is predictive maintenance and how does it work?

Predictive maintenance is a maintenance strategy that focuses on predicting equipment failures and other potential issues before they happen. By collecting and analyzing data from various sources such as sensors and historical records, predictive maintenance allows organizations to optimize their maintenance schedules, reduce unplanned downtime, and improve overall operational efficiency.

Advanced analytical techniques, including machine learning, artificial intelligence, and statistical modeling are used to process the collected data and generate estimates about the remaining useful life of equipment or the likelihood of failure within a specific time frame.

In the realm of the IoT predictive maintenance plays a valuable role in managing assets and devices. Sensors continuously monitor data points like temperature, vibration, and pressure while transmitting that data for analysis. Data processing tools clean, process, and transform the raw data, preparing it for analysis using a variety of possible statistical models. These models identify patterns and correlations in the data, allowing them to predict equipment failures or performance degradation.

Based on these predictions, maintenance teams can plan and schedule maintenance tasks more effectively, avoiding unexpected breakdowns and minimizing the overall impact on operations.

By shifting from reactive to proactive maintenance, organizations get the following benefits:

  • Cost savings

  • Increased asset longevity

  • Enhanced productivity

Common predictive maintenance methods

Before going over some of the general tools that can be used to collect and process data for predictive maintenance, here are a few examples of the types of data that are commonly used for predictive maintenance for use cases like IoT or Industry 4.0:

  • Infrared analysis

  • Condition based monitoring

  • Vibration analysis

  • Fluid analysis

  • Visual inspection

If you want more information on these methods, check out this more detailed guide on predictive maintenance.

Tools for predictive maintenance

There are a variety of tools required to take advantage of predictive maintenance for your business. This includes everything from how you collect data from devices to processing, analyzing, and visualizing your data. In this section we will look at some of the tools available for each of the major components of predictive maintenance.

Full stack platform vs custom solution

One of the first decisions you will need to make when implementing a predictive maintenance system is whether you go with a “full stack” platform that manages the many different parts or if you want to build a more custom solution.

The main tradeoff between these choices is going to be development speed, cost, performance, and flexibility. Fully managed platforms are going to simplify implementation by abstracting away much of the complexity and providing features out of the box. On the other hand these platforms will often cost more in the long run, have a lock-in effect, and may not be optimized for your specific use case. By building your own custom solution you can choose the ideal tools for each aspect and potentially save money while also getting better performance.

The main thing to consider is what scale you will be operating at, how soon you want a solution deployed, performance requirements, and the development resources you have available. If you are going to be collecting data from a large number of devices with a huge amount of data then it might make sense to build a custom solution to save money in the long run. On the other hand if you just want to make a quick prototype, going with a managed solution might make sense.

Some platforms can give you the best of both worlds by acting mostly as an integration tool, simplifying the low level details while allowing you to choose things like where your data is stored and what tools are used for processing data.

With all that in mind, let’s take a look at some of the options for each of the following tasks that are critical to a predictive maintenance implementation:

  • Data collecting

  • Connectivity

  • Data storage

  • Data processing

  • Analytics and forecasting

  • Visualization

Data collection

The first step of making predictive maintenance possible is to be able to collect the data being generated by your devices. Data collection really isn’t a value add to your business, so ideally you want a tool that provides built-in features for collecting data from many different sources and communication protocols rather than your developers being forced to create custom code. Depending on the architecture of your predictive maintenance application you may also want some ability to process and filter data at the edge. The choice here is a balance of the data collection tool being lightweight in terms of resource usage, number of integrations, and data processing capabilities. Here are a few popular options available:


Another thing to keep in mind is what communication protocol your application uses. Which works best will depend on your architecture and other factors like network reliability, bandwidth, and performance requirements. For example, many IoT workloads can’t guarantee reliable connectivity like you would expect in a data center for a web application, so that must be accounted for. Here are some communication protocols to consider for predictive maintenance:

Data storage

Once you are collecting and transmitting your data, you need to store that data somewhere. For predictive maintenance, that data can in many cases be considered time series data, which might warrant a dedicated solution depending on your performance requirements and the volume of data you are storing. Here are a few commonly used data storage options for predictive maintenance data:

Data processing

Data processing can happen both before and after the data is stored. It can be done at the edge with your data collection tool, done prior to storage via stream processing, with batch processing at some scheduled time period, or the raw sensor data can be stored directly. In many cases a combination of these are done, with raw data being stored for historical record and different transformed versions of the data being created for usage by analysts or models. Here are some tools that can be used for processing predictive maintenance data:

Analytics and forecasting

Once your data has been processed and stored you can start analyzing your data. Typically predictive maintenance uses regression models and feature engineering, but in recent years more advanced machine learning and deep learning models have also started to be used. Depending on your use case, you can use automated tools that you simply need to provide data to create models or build your own models using frameworks like the following:


Having data visualization tools integrated with your predictive maintenance system will help with not only monitoring the system but also make it easier to create reports and allow users to freely analyze the data being collected from the system. There are a number of tools available depending on the use case for the visualization and the technical skills of the user, ranging from analyst focused reporting tools to creating custom visualizations using lower-level libraries or frameworks. Here are a few options:

Predictive maintenance platforms

As discussed above, there are a number of platforms available that either provide everything you need out of the box or focus on making it easier to integrate all the tools you choose together. Here are a few popular platforms for IoT and predictive maintenance specifically:

InfluxDB for predictive maintenance

Hopefully this article helped give you a decent overview of the available tools you can use for your predictive maintenance project. If you are curious about how some companies are using predictive maintenance in the real world, check out these case studies for some inspiration:

As some next steps, you can also check out the following tutorials and guides that show you how to use some of the tools mentioned in this article or just explain additional concepts related to predictive maintenance: