Modern engineering systems are complex and sophisticated. Any minor issue can be escalated to total system failure. To resolve this issue, many researchers have contributed many diagnostic methods efforts using digital signal processing. However, two barriers have to be overcome. First, enormous amount of signal data is required from system processes. Recently Industry 4.0 paradigm promoting digital transformation of industry and societies resolves this issue partially, but what signals from the system can be used. Second, operational data from the dynamic system are nonlinear and often chaotic, how can the signals be interpreted to identify fault, assess reliability and decide remedial actions. This article reviews fault diagnosis and system health monitoring research efforts and outcomes in the last couple of decades. The ultimate goal is to develop fault-free systems. The best way to achieve this goal is to prevent faults from happening. Two approaches of nonlinear signal modelling, i.e., chaotic systems and frequency distribution analysis for fault detection have been researched. Lessons learned are summarized with discussions on, merits and drawbacks. These experiences are shared in this article to stimulate further research towards the ultimate goal of fault-free system operations and controls.
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Making Sense from Nonlinear Dynamic Signals—From Fault Detection to Prediction
Author Information
Received: 20 Jan 2026 | Revised: 06 Feb 2026 | Accepted: 30 Mar 2026 | Published: 07 May 2026
Abstract
Keywords
nonlinear signals | chaotic systems | scalograms | distribution of frequencies | fault-free systems
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