IoT and Predictive Maintenance: How to Make Big Data Work for You

The value of this approach is revealed in the results of analytics of the equipment data since it can predict machine behavior and, therefore, prevent possible failures. However, if we want to get this value, it requires to meticulously build the whole structure of the IoT ecosystem – from data capture to analytical model. 

How could I know if my data is applicable for predictive analysis? Can algorithms return unreliable results? Indeed, every business is unique. If a competitor company managed to save 25% on maintenance when implementing PdM of their machinery, it doesn’t mean you can count on such savings too. In this article, PSA figures out what you need to perform reliable data analysis within enterprise IoT solutions; how to prepare your data for analysis, and why one PdM strategy works better than another. 

Companies have long used predictive models in their equipment maintenance strategies. However, in the pre-IoT era, they had to rely on delayed data and be content with incomplete analysis if wanted to create a PdM strategy. It turns out that without the Internet of Things, you can roughly guess when a machine may fail, which is not enough to create a competent PdM strategy and switch to it.

• Real-time data collection from smart sensors installed on equipment or vehicles.
• Processing and filtering data to make it suitable for analytics.
• Applying the predictive analytical model to the collected data.

Thus, technically, the IoT ecosystem is a perfect aid for the creation of a predictive maintenance strategy. The question arises in the implementation of the concept for a particular business case. As we mentioned, only building a custom analytical model that considers maximum features of your enterprise environment can create a strategic value.

Predictive analytics is the final component of the IoT ecosystem that is created for predictive maintenance. It brings the very coveted result that becomes a basis for the predictive maintenance strategy. The building of a predictive-analytical model is based on finding parameters among the data that affect other data and determining the degree of its influence. By running data through such a model, you get a picture of some systems that will change if some parameter changes its value. Naturally, more data allows us to find more patterns and, therefore, to build a more accurate analytical model.

In the case of PdM, the parameters that signal an imminent failure of the equipment are analyzed. For example, to obtain predictions about engine wear, vibration monitoring of its various components is used. Analysis of external parameters that can indirectly influence or precede this failure can significantly enhance a predictive model. For example, it can be specified when it is noticed that hot or humid weather conditions might accelerate engine wear. The purpose of this approach is to predict whether the equipment will fail at a certain point in the future.

The result that predictive analytics returns is an answer to one of the following questions: 

1. Is there a chance of failure at a certain number of actions? For this, a classification model of predictive analysis is used.
   
2. How long will the machine work without failures? The regression analytical model helps to predict the remaining useful life.

As an example, consider gate crossings. Suppose that one day a gate crossing stops opening completely that indicates its imminent failure. A more lengthy and meticulous analysis allows you to identify exactly when a failure might occur. For example, having the data on the critical degree of opening, AI can calculate how much it has left to work. 

Let’s consider another example – you faced issues with the conveyor line. It has happened more than once that equipment fails at elevated air temperatures. Knowing this and the weather forecast for the near future, you will be able to prevent a rise in temperature in the shop, thereby avoiding downtime. 

So far, we consider basic cases, when you need supervised learning techniques to build a predictive analytical model. This means that initial data, results, and patterns are known in advance, and everything you need is to implement proper algorithms. However, machine learning techniques also involve unsupervised learning – the implementation of algorithms where the results are unknown. It allows for finding non-obvious patterns from a wide range of data and setting up more detailed monitoring and maintenance processes. It works for businesses who have a big amount of data regarding their equipment.

As you already understood, predictive models are based on the same techniques, and “magic” happens where the completeness and quality of the data itself are not questioned. Thus, reliable, accurate, and promising predictive-analytical models can be obtained when the following conditions are met.

The basic step for building a promising analytical model for predictive maintenance is the definition of status indicators – the features of your system that change in a predictable way as the system loses its efficiency. The next step is to collect comprehensive initial historical data regarding equipment and maintenance, which usually includes:

• History of failures
• Maintenance history
• Meta-data on the hardware
• Initial wear data, etc.

If you have already implemented IoT-enabled monitoring or Condition-based maintenance for your equipment, it is a great benefit for creating a predictive analytics model. This data provides real-time feedback from sensors allowing for deeper insights into patterns of system behavior that might lead to failure.

Data preprocessing is a necessary step allowing to clear the data and convert it into a form appropriate for the status indicators to be extracted. For predictive analytics, time series data should be used. That is, data should include a timestamp, a set of sensor readings associated with timestamps, and equipment identifiers.

Data preprocessing can be performed both in the edge and the cloud and includes simple techniques such as the removal of outliers and missing values, as well as signal processing techniques. However, the solution for preprocessing should be picked up individually for every machine or group of machines. If data is contaminated by sensor noise, it can be removed using autoencoders – simple neuro networks trained specifically for this. 

As you can see, this step is technically not complicated, but essential to gain more precise analytical results at the output.

Today, predictive analytics heavily relies on advanced machine learning technologies. Data scientists use deep learning and complex algorithms to analyze multiple variables to estimate the system's remaining useful life with maximum accuracy. A crucial advantage that can strengthen the model is optimal combination of data, and new patterns that will refine the predictive model. For this, the clustering method of analysis is used. 

Thus, the more data a company has through competent asset management, the more detailed models can be created from the very beginning. Thus, we advise taking the time to search for optimal data combinations and finding non-obvious patterns of your equipment failures. On the other hand, companies can constantly expand their predictive maintenance strategies when they have already implemented them. For instance, new sensors can be installed, new methods for data cleansing, and new modeling techniques.

When the AI-based analytical model is created, it’s important to allocate time for training. Typically, the cycle of PdM implementation involves building, training, deploying, getting feedback, and retraining. Retraining can be performed as many times as it needs until the system achieves the target results.