What Is Analog System Fault Diagnosis?

Analog system fault diagnosis is the discipline concerned with detecting, locating, and identifying faults in analog electronic circuits and systems. Unlike digital circuits, which operate with discrete binary states, analog circuits work with continuous-valued signals, making faults harder to model and isolate. The field draws on circuit theory, signal processing, and increasingly on machine learning to infer the health of a system from measurable outputs at accessible test nodes.

Analog faults fall into two broad categories. Catastrophic faults, such as open-circuit and short-circuit conditions, prevent the circuit from functioning altogether. Parametric faults are subtler: component values drift outside acceptable tolerances due to aging, temperature, radiation, or manufacturing variation, causing the circuit to operate incorrectly without completely failing. Both types present diagnostic challenges because analog circuits are inherently nonlinear, and the relationship between a fault and its observable effect at a test node is rarely direct.

Fault Models and Classification

A fault model is a formal representation of how a defect alters circuit behavior. The most widely used model treats each component parameter as a random variable drawn from a tolerance distribution, and defines a fault as a deviation beyond a specified bound. More structured models include the stuck-at model borrowed from digital testing, adapted to represent nodes fixed at a particular voltage, and the short/open model for catastrophic conditions. The IEEE Xplore survey on fault diagnosis of analog circuits reviews how these models evolved and why no single model covers the full range of analog failure modes.

Classifying faults by their observability at accessible nodes is a prerequisite for any diagnosis strategy. Because analog circuits typically have far fewer test points than digital ones, many faults are aliased: two distinct faults produce identical or nearly identical output signatures. Handling this aliasing problem is central to the theoretical foundations of the field.

Diagnosis Methods

The major diagnostic strategies divide into dictionary-based methods, parameter identification, and machine learning approaches. Dictionary methods pre-compute the circuit response under each candidate fault, then compare measured responses to the stored dictionary to find the best match. Parameter identification methods solve an inverse problem, estimating component values from measured voltages and currents using sensitivity analysis or optimization.

Machine learning has become a dominant approach in recent years. Techniques such as support vector machines, deep convolutional networks, and recurrent architectures learn fault signatures from simulated or measured training data without requiring explicit fault models. A 2025 study published in Scientific Reports on wavelet-based fault classification demonstrates how combining continuous wavelet transforms with dual-stream convolutional networks improves classification accuracy on nonlinear circuits by exploiting both temporal and frequency-domain features simultaneously.

Test Generation and Simulation

Generating useful test stimuli for analog circuits is more complex than for digital circuits because there is no equivalent of the Boolean satisfiability frameworks used in digital automatic test pattern generation. Analog test generation seeks input waveforms that maximize fault coverage, meaning the fraction of the fault set that produces distinguishable output changes. Simulation plays a central role: SPICE-family simulators propagate fault models through the circuit netlist to build dictionaries and coverage estimates before hardware tests are run. The IntechOpen chapter on symbolic analysis techniques for fault diagnosis describes how symbolic methods complement numerical simulation by producing closed-form expressions for circuit transfer functions, making fault-effect relationships more transparent.

Applications

Analog system fault diagnosis has applications across a wide range of safety-critical and precision domains, including:

• Aerospace and defense electronics, where in-flight fault isolation must operate with limited test access
• Automotive control units, including sensor signal chains and power management circuits
• Medical instrumentation, where parametric drift in amplifier stages can affect measurement accuracy
• Industrial process control, where analog sensor interfaces must be monitored for degradation over time
• Telecommunications front-end hardware, where RF and mixed-signal subsystems require field diagnostics