Vahid MohammadZadeh Eivaghi

Vahid MohammadZadeh Eivaghi, Mahdi Aliyari Shoorehdeli

False and nuisance alarms in industrial fault detection systems are often triggered by uncertainty, causing normal process variable fluctuations to be erroneously identified as faults. This paper introduces a novel encoder-based residual design that effectively decouples the stochastic and deterministic components of process variables without imposing detection delay. The proposed model employs two distinct encoders to factorize the latent space into two orthogonal spaces: one for the deterministic part and the other for the stochastic part. To ensure the identifiability of the desired spaces, constraints are applied during training. The deterministic space is constrained to be smooth to guarantee determinism, while the stochastic space is required to resemble standard Gaussian noise. Additionally, a decorrelation term enforces the independence of the learned representations. The efficacy of this approach is demonstrated through numerical examples and its application to the Tennessee Eastman process, highlighting its potential for robust fault detection. By focusing decision logic solely on deterministic factors, the proposed model significantly enhances prediction quality while achieving nearly zero false alarms and missed detections, paving the way for improved operational safety and integrity in industrial environments.

Vahid MohammadZadeh Eivaghi, Mahdi Aliyari Shoorehdeli

Understanding output variance is critical in modeling nonlinear dynamic systems, as it reflects the system's sensitivity to input variations and feature interactions. This work presents a methodology for dynamically determining relevance scores in black-box models while ensuring interpretability through an embedded decision module. This interpretable module, integrated into the first layer of the model, employs the Fisher Information Matrix (FIM) and logistic regression to compute relevance scores, interpreted as the probabilities of input neurons being active based on their contribution to the output variance. The proposed method leverages a gradient-based framework to uncover the importance of variance-driven features, capturing both individual contributions and complex feature interactions. These relevance scores are applied through element-wise transformations of the inputs, enabling the black-box model to prioritize features dynamically based on their impact on system output. This approach effectively bridges interpretability with the intricate modeling of nonlinear dynamics and time-dependent interactions. Simulation results demonstrate the method's ability to infer feature interactions while achieving superior performance in feature relevance compared to existing techniques. The practical utility of this approach is showcased through its application to an industrial pH neutralization process, where critical system dynamics are uncovered.

Vahid MohammadZadeh Eivaghi, Mahdi Aliyari Shooredeli

To benefit from the modeling capacity of deep models in system identification, without worrying about inference time, this study presents a novel training strategy that uses deep models only at the training stage. For this purpose two separate models with different structures and goals are employed. The first one is a deep generative model aiming at modeling the distribution of system output(s), called the teacher model, and the second one is a shallow basis function model, named the student model, fed by system input(s) to predict the system output(s). That means these isolated paths must reach the same ultimate target. As deep models show a great performance in modeling of highly nonlinear systems, aligning the representation space learned by these two models make the student model to inherit the approximation power of the teacher model. The proposed objective function consists of the objective of each student and teacher model adding up with a distance penalty between the learned latent representations. The simulation results on three nonlinear benchmarks show a comparative performance with examined deep architectures applied on the same benchmarks. Algorithmic transparency and structure efficiency are also achieved as byproducts.