Reconstruction-Based Diagnosis through Gödel-Regularized Virtual Observability
Oliver Niggemann, Jonas Ehrhardt, Alexander Diedrich
Sensible and honestly reported virtual-observability diagnosis framework, but the headline Gödel regularization shows no reliable gain and there are no competing diagnosis baselines.
Abstract
Not every component in a technical system can be equipped with sensors. Hence, when a fault occurs, available sensors often detect it only after its effects have propagated. This makes the isolation of faults difficult. We approach this problem by introducing virtual sensors. For each combination of component and fault type, we let external knowledge describe the fault-sensitive variables. We introduce a novel neural observer that is only trained on normal data. For fault isolation, it uses available sensor measurements where it is possible and reconstructs missing variables as virtual sensors to predict normal behavior. It detects deviations between current values and nominal predictions and computes local symptoms, which are then compared by onset time. The result is a set of root causes describing the faulty behavior. We use Gödel logic to encode physical knowledge and guide training. We evaluate our approach on synthetic networks, the Tennessee Eastman Process, and datasets of two modular production plants.
AI Impact Assessments
(1 model)Core Contribution
ReDiVO targets fault isolation when root-cause components lack sensors. Domain knowledge maps each component-fault candidate to fault-sensitive variables. Unmeasured variables are reconstructed as "virtual sensors" by an LSTM observer trained only on normal data. The observer produces a "current" trajectory, which uses measurements where they exist and reconstructions elsewhere. It also produces an open-loop "nominal" trajectory rolled forward from δt samples earlier. Calibrated uncertainty intervals around both trajectories define persistent local symptoms. Symptom onset-time intervals are then compared, and the output is a unique candidate, an ambiguity group, or no diagnosis. Gödel (fuzzy) logic rules encode topology, qualitative relations, physical balances and sensor physics. They enter only as a training-time penalty, which addresses underspecification of reconstructions learned from normal data.
The novelty is mostly in the combination: virtual sensors, onset-time-based isolation and neuro-symbolic regularization. Each ingredient is known (neural observers, fuzzy-logic losses, symptom-ordering diagnosis). The formalization of "virtual observability" for component-fault candidates and the explicit ambiguity-group output are the distinctive framing.
Methodological Rigor
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Potential Impact
The concept addresses a real industrial problem: sparse sensing and symptom propagation delays. The conditional design is practical because domain knowledge only specifies predictors and fault-sensitive variables, offline reference signals supervise training, and the output explicitly admits ambiguity. This could appeal to process-industry and cyber-physical production-system diagnosis communities (DX, IFAC SAFEPROCESS). The honest empirical findings are also useful: better reconstruction does not guarantee better diagnosis, and recursive training beats one-step training for nominal forecasting (HAI: forecast MAE 16.6 → 10.1 rpm; correct pump 29/30 vs 25/30). Broader influence is limited by the weak evidence for the headline neuro-symbolic contribution and by the lack of state-of-the-art comparisons. The method also needs manual a-priori knowledge, which is a scaling barrier.
Timeliness & Relevance
Neuro-symbolic AI, virtual sensing and explainable root-cause analysis for CPS are active areas. The diagnosis community is moving toward hybrid learned and knowledge-based methods, so the topic is timely. The work is a niche, incremental step within that trend rather than a bottleneck breaker.
Strengths & Limitations
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Overall
This is a competent, honest, applied hybrid-diagnosis paper. Its main idea is reasonable and its framing is useful. The empirical case is mixed and the novelty is moderate, so I expect modest, niche impact.
Generated Nov 4, 2026
Comparison History (0)
Sensible and honestly reported virtual-observability diagnosis framework, but the headline Gödel regularization shows no reliable gain and there are no competing diagnosis baselines.