Physics-informed and explainable artificial intelligence for robust fault diagnosis in engineering systems

Purpose Accurate fault diagnosis is essential for ensuring the safety and reliability of engineering systems. However, many artificial intelligence (AI)–based diagnostic models rely on purely data-driven black-box approaches that may produce predictions inconsistent with established physical behaviour, limiting confidence in safety-sensitive applications. This study proposes a unified framework that integrates predictive accuracy, physical consistency and interpretability within a single learning formulation. Design/methodology/approach A physics-informed and explainable neural network framework is developed for vibration-based fault severity estimation. Physically meaningful statistical features such as RMS energy, peak-to-peak amplitude and kurtosis, are used as model inputs. Physical knowledge is embedded through a monotonicity-based loss constraint that penalises violations of the expected relationship between vibration energy and fault severity. A combined objective balances prediction error and physics-based regularisation. Global permutation feature importance is applied to assess alignment between learned feature relevance and physical expectations. The framework is evaluated using physics-inspired synthetic vibration data under controlled conditions, supported by cross-validation and quantitative monotonicity assessment. Findings Compared with an unconstrained neural network, the physics-informed model achieves lower prediction error, smoother convergence behaviour and substantially reduced monotonicity violations. The embedded constraint acts as a structural regulariser while preserving model flexibility. Explainability analysis indicates closer alignment between feature relevance and physically meaningful vibration indicators. Originality/value The study presents a reproducible framework that integrates loss-level physics constraints with global explainability analysis for fault severity diagnosis. By embedding physical admissibility directly into optimisation, the approach contributes toward the development of more transparent and physically grounded AI systems for engineering diagnostics. A conceptual flow diagram shows physics informed neural network processing vibration signals to produce interpretable fault. The conceptual flow diagram is arranged from left to right in three sections. The left section titled “PHYSICS INSPIRED VIBRATION SIGNALS AND FEATURES” contains three stacked rectangular panels. The top panel labeled “HEALTHY (Blue)” shows a low amplitude waveform, the middle panel labeled “INCIPIENT FAULT (Orange)” shows a moderate amplitude waveform, and the bottom panel labeled “SEVERE FAULT (Red)” shows a high amplitude waveform. To the right of these panels, three vertical bar charts labeled “R M S Energy”, “Peak to Peak Amplitude”, and “Kurtosis” show increasing bar heights from healthy to severe. A large downward arrow beside the panels indicates “INCREASING FAULT SEVERITY, SIGNAL AMPLITUDE and ENERGY”. A solid rightward arrow runs from this section to the central section titled “PHYSICS INFORMED NEURAL NETWORK (P I N N) FRAMEWORK”. In the central section, a box labeled “LOSS” with a downward arrow connects to “PHYSICS BASED REGULARIZATION (Monotonic Energy Severity Constraint)”. Below, a neural network diagram shows input nodes on the left connected through multiple hidden layers to output nodes on the right, with green highlighted nodes indicating the physics informed constraint. A crossed out black box icon with a red X below is labeled “AVOID UNPHYSICAL, UNINTERPRETABLE BLACK BOX MODEL”. A solid rightward arrow runs from this section to the right section titled “ROBUST and INTERPRETABLE DIAGNOSTIC OUTPUTS”. The top panel in this section labeled “FAULT SEVERITY SCORE” shows a horizontal gauge with segments labeled “Low (Healthy)”, “Medium (Incipient)”, and “High (Severe)”, with a marker positioned around 1.69 and a label “Status”. Below, a panel labeled “GLOBAL FEATURE IMPORTANCE (PHYSICALLY CONSISTENT)” displays horizontal bars for “R M S Energy”, “Peak to Peak Amplitude”, and “Kurtosis”, increasing from healthy to severe, with a note “Consistent Evolution with Fault Progression”.