Models and Algorithms: Building the brains behind AI

In today’s industrial landscape and control rooms, optimizing operations, ensuring safety, and minimizing costs are critical challenges. Increasingly, Artificial Intelligence (AI) is emerging as a powerful solution to tackle these issues. From monitoring sensitive systems and processes to streamlining operational workflows, AI is steadily making its way into various industrial and service domains.

But before jumping headfirst into integrating AI within your organization, it’s crucial not to skip the fundamentals. This article aims to equip you with a clear understanding of two core components of AI: models and algorithms.

A real-world use case: Predictive maintenance in manufacturing

Imagine a vital pump on a production line. An unexpected breakdown could result in costly downtime and emergency repairs. While reactive and preventive maintenance strategies exist, they struggle to keep up with the complexity and scale of monitoring hundreds of devices in real time.

AI offers a smarter alternative. By collecting vast amounts of data from the pump’s sensors—vibration, temperature, pressure—as well as contextual data like power supply or network signals, we can train an AI model. For instance, a Support Vector Machine (SVM) algorithm can be used to build a predictive model.

Once trained, this model continuously analyzes incoming data to detect early warning signs of failure, before it happens. This predictive capability allows maintenance to be scheduled proactively, minimizing costly unplanned downtime.

Machine Learning, Deep Learning, Generative AI – Who does what?

The AI field is broad, and understanding its branches is essential to applying the right technologies effectively.

Machine Learning (ML) is the foundation of most industrial AI applications. It involves algorithms that learn patterns from data without being explicitly programmed to perform a specific task.

Deep Learning (DL) is a subset of ML that uses layered neural networks to analyze unstructured data like images (e.g. defect detection), sound, or text. It excels at complex tasks but typically requires more data and computing power.

Generative AI is a newer and rapidly evolving field focused on content creation—text, code, images, and more. While promising for applications like report generation or procedure drafting, it differs from the predictive and anomaly detection tasks more common in industrial settings.

Supervised vs. unsupervised learning in Machine Learning

At the core of ML are algorithms – structured procedures that generate models by learning from data. These algorithms generally fall into two categories:

1. Supervised Learning: The most common method for prediction tasks. It requires labeled data—datasets where the outcomes are already known. For example, to predict pump failures, the model is trained on sensor data tagged with historical failure events. Algorithms learn to associate patterns with specific outcomes. This includes:
   • Classification algorithms (e.g. predict “normal” vs. “failure”)
   • Regression algorithms (e.g. predict temperature in °C)
2. Unsupervised Learning: Used when data isn’t labeled. These algorithms detect hidden patterns or structures—grouping similar equipment behavior or flagging anomalies without prior knowledge of what constitutes a failure.

The training phase: How a model is born

Creating an AI model involves a training phase where an algorithm iteratively adjusts the model’s internal parameters based on a dataset. The goal is to improve performance on a given task – prediction, classification, clustering, etc. This training process is the realm of data science, typically carried out by experts known as data scientists.

A simple example: Decision trees in classification

Let’s say we want to predict whether a process is in a critical state based on two variables: temperature and pressure.

1. Data Collection: Historical temperature and pressure readings, along with the system status (critical or normal), are gathered.
2. How the Algorithm Works: A decision tree algorithm analyzes this data to find simple rules (“If… then…”) that split the outcomes.
3. Model Training: A discovered rule might be:
   • “If temperature > 80°C, there’s a 90% chance of a critical state.”
     Further refinement might yield:
     “If temperature > 80°C and pressure > 5 bars → 99% probability of a critical event.”
4. Model Ready: The resulting model can now analyze real-time data and quickly provide a prediction using these decision rules.

This illustrates a key distinction: the algorithm is the learning method; the model is the final product used in operations.

AI: Not magic: Just smart engineering

I isn’t a magic wand. It’s a powerful tool for monitoring, enhancing efficiency, ensuring safety, and improving performance in control rooms and industrial processes.

When combined with Motilde’s data visualization and event management tools, AI paves the way for the augmented control room – more proactive, more intelligent, and capable of transforming raw data into informed decisions.

Let Motilde guide you in designing and optimizing your control and supervision environments.
Reach out today – one of our specialized engineers will get back to you shortly.

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