Reliability is as important as performance when it comes to modern industrial systems. Unexpected failures and asset degradation can lead to changes in the system’s dynamics that result in unreliable operations.

Predictive analytics is therefore crucial to eliminating downtime and increasing a company’s bottom line. However, because not all engineers are data experts, developing predictive maintenance systems can be a challenge.

This is where predictive analytics platforms come in handy. By using pre-developed modules, engineers and data scientists can easily develop and deploy their own health monitoring systems.

Below are four reasons why engineers and data scientists should take advantage of predictive analytics platforms:

1. Convenient Data Parsing Tools

Data collected from different industrial systems or machines comes in various formats. For that reason, converting files into a certain data format to speed up processing steps is a common first step.

Using a predictive analytics platform to convert and parse data files makes data parsing significantly easier. Platforms are also useful when data variables have different lengths or sampling frequencies, as they enable users to combine data from different sources for model training steps later on.

Another benefit of a platform is that it organizes the data into the specific format that the analysis functions require. That way, the user does not need to waste time and effort on organizing data into the required format and can think about the different ways to analyze data and take advantage of it instead.

2. Essential Data Preprocessing Functions

Data quality directly impacts the accuracy of model predictions, and for that reason is extremely important.

Normally, raw data collected from industrial systems or machines cannot be directly used for model training. Some of the outliers in raw data need to be removed and data segmentation is often necessary to pick out the parts that are key to data analysis. In addition, sometimes new variables are calculated from the original data to provide more information.

The functions mentioned above can be easily performed with the help of powerful preprocessing modules available in predictive analytics software platforms. With these functions readily available, users can preprocess data much faster than they normally would and get more accurate predictions.

3. Suite of Machine Learning Algorithms

Predictive analytics platforms provide a myriad of machine learning algorithms to use for model building, including logistic regression, support vector machine, clustering and principal component analysis. These pre-built algorithms enable users to go outside of their comfort zones and try new solutions.

Before model training, users often times do not know which features to include in their models. Predictive analytics platforms are also beneficial in that sense, as they offer a multitude of methods to quickly rank features, resulting in more accurate models and preventing issues like over-fitting or under-fitting.

Once users determine which features or signals to include in the predictive model, they can simultaneously try several of the available learning algorithms. This is useful because there is no way to tell which algorithm will perform best for a given application. The visualization tools provided to compare results from the different algorithms enable users to select the ones that deliver the most accurate predictions.

4. Rapid, Scalable Deployment

In general, predictive analytics platforms have all the tools required for deployment built into them. They also require users to have dramatically less data analytics knowledge and programming skills, which is a significant advantage since data scientists are not only expensive, but also hard to come across.

Another major benefit of platforms is that they are designed to easily deploy predictive analytics models, enabling users to reap the benefits of predictive analytics technologies right away. They are also scalable, meaning models can be deployed to hundreds of identical or similar assets.

PDX, Predictronics’ end-to-end predictive analytics solution, is an example of such a platform. Designed primarily for industrial applications, its main goal is to reduce unplanned downtime, increase productivity and improve product quality by collecting and analyzing industrial big data.