AI neural network patterns analyzing a complex engineering system.

Smart Sensors, Safer Systems: How AI is Revolutionizing Fault Detection

Jordan Keane in Tech & Innovation June 2025 • 4 min read.

"Discover how data-driven fault detection methods are enhancing safety and reliability in everything from aerospace to everyday electronics using AI, reducing errors and improving maintenance."

Imagine a world where your devices not only tell you when something is wrong but also predict potential problems before they even happen. This isn't science fiction; it's the reality that advanced fault detection and isolation (FDI) technologies are bringing to various industries. Since the concept of autonomous fault diagnosis emerged, interest in this field has exploded, with new methods promising to make our systems safer and more reliable.

Traditional fault diagnosis falls into two main categories: model-based and data-driven approaches. Model-based methods rely on detailed mathematical descriptions of systems, which can be challenging to develop and maintain for complex engineering systems. Advances in sensing and data acquisition now provide huge volumes of raw data, making data-driven methods more attractive. This shift allows engineers to harness the power of artificial intelligence and machine learning to identify and address potential faults.

Data-driven techniques use a variety of tools, including neural networks and fuzzy logic, to extend and improve traditional model-based FDI schemes. One straightforward solution involves creating a dynamical mathematical model from available data and using this model to design conventional FDI systems. However, this approach can suffer from errors introduced during system identification, ultimately undermining the reliability of the fault diagnosis scheme.

The Rise of Data-Driven Fault Detection

AI neural network patterns analyzing a complex engineering system.

In recent years, a new approach has emerged, focusing on directly constructing FDI schemes from system input-output (I/O) data. These methods, known as subspace-based data-driven fault detection and isolation, identify the system's left null space using I/O data. This process typically involves a reduction step, where the system order is estimated via Singular Value Decomposition (SVD). However, this step can be problematic because choosing a truncation point for 'small' singular values is subjective and can lead to errors.

Errors in estimating the system order can have nonlinear effects on the FDI scheme’s performance. Some research has focused on eliminating the reduction step to improve fault estimation. For example, a fault detection scheme developed by Dong and colleagues can be synthesized directly from system I/O data, using a high-order Finite Impulse Response (FIR) filter parameterized by the system's Markov parameters. However, extending this approach to fault isolation can be complex, often requiring the synthesis of Markov parameter estimation errors, which are not always readily available.

To summarize, data-driven fault detection offers several key advantages:

Adaptability: AI algorithms can adapt to changes in the system over time, ensuring that the fault detection system remains effective even as the system evolves.
Comprehensive Analysis: AI algorithms can analyze vast amounts of data to identify patterns and anomalies that might be missed by traditional methods.
Predictive Maintenance: AI can help predict when maintenance will be needed, reducing downtime and improving efficiency.

Recent work by Wan and colleagues has pointed out that some methods cannot be applied to open-loop systems and do not compensate for estimation errors. Their proposed offline and online algorithms aim to address these shortcomings, though the online optimization algorithm can be computationally intensive. To overcome these limitations, new data-driven fault detection, isolation, and estimation filters are being developed, constructed directly in state-space form using available system I/O data.

The Future of Fault Detection

As technology advances, the ability to quickly and accurately detect and isolate faults will become even more critical. Data-driven fault detection methodologies, particularly those enhanced by AI, offer a promising path toward creating safer, more reliable systems across industries. By overcoming the limitations of traditional model-based approaches, these innovative techniques are paving the way for a future where potential failures are identified and addressed before they can cause significant disruptions.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.automatica.2017.07.040, Alternate LINK

Title: A Data-Driven Approach To Actuator And Sensor Fault Detection, Isolation And Estimation In Discrete-Time Linear Systems

Subject: Electrical and Electronic Engineering

Journal: Automatica

Publisher: Elsevier BV

Authors: Esmaeil Naderi, K. Khorasani

Published: 2017-11-01

Everything You Need To Know

What are the main differences between model-based and data-driven approaches to fault diagnosis, and what challenges are associated with each?

Fault diagnosis traditionally relies on two main categories: model-based and data-driven approaches. Model-based methods use detailed mathematical descriptions, which are difficult to create and maintain for complex systems. Data-driven methods, leveraging large volumes of data and AI, are increasingly favored due to advancements in sensing and data acquisition. These methods employ tools such as neural networks and fuzzy logic to enhance conventional schemes. However, a simple mathematical model derived from available data may introduce errors during system identification, affecting reliability.

How do subspace-based data-driven fault detection and isolation methods work, and what are the potential pitfalls related to Singular Value Decomposition (SVD) in these methods?

Data-driven fault detection uses system input-output (I/O) data to directly construct fault detection and isolation (FDI) schemes. Subspace-based methods identify the system's left null space, often involving a reduction step using Singular Value Decomposition (SVD) to estimate system order. The selection of a truncation point for 'small' singular values is subjective and can cause errors, non-linearly affecting the FDI scheme’s performance. Some approaches eliminate the reduction step to improve fault estimation, such as using a high-order Finite Impulse Response (FIR) filter parameterized by Markov parameters.

What advantages does data-driven fault detection offer, and what are its limitations regarding open-loop systems and estimation errors?

Data-driven fault detection offers several benefits including Adaptability, where AI algorithms adjust to system changes over time; Comprehensive Analysis, using AI to analyze vast data for patterns missed by traditional methods; and Predictive Maintenance, employing AI to predict when maintenance is needed. However, some methods are unsuitable for open-loop systems and may not compensate for estimation errors. Overcoming these limitations involves developing new fault detection, isolation, and estimation filters directly in state-space form using system I/O data.

What specific issues did Wan and colleagues identify in existing fault detection methods, and how did they attempt to address these issues with their proposed algorithms?

Wan and colleagues highlighted limitations in existing methodologies concerning open-loop systems and compensation for estimation errors. They proposed offline and online algorithms to address these issues, but the online optimization algorithm can be computationally intensive. This necessitates the development of new data-driven fault detection, isolation, and estimation filters that are constructed directly in state-space form using available system input-output data, paving the way for more efficient and accurate fault management.

Looking ahead, how will data-driven fault detection methodologies, particularly those enhanced by AI, contribute to creating safer and more reliable systems across various industries?

Advancements in technology increase the importance of quickly and accurately detecting and isolating faults. Data-driven fault detection methodologies, especially those enhanced by AI, are crucial for creating safer and more reliable systems. These techniques address the limitations of traditional model-based approaches, enabling the identification and resolution of potential failures before they cause significant disruptions and ensuring proactive and effective system maintenance.