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Failure prognostics can improve industrial systems’ availability and reliability by determining the occurrence of failure and estimating the system’s remaining useful life (RUL) before deterioration. This paper presents a prognostic method based on data-driven and degradation model approaches to accurately predict the RUL of a scale replica system under different operating conditions. For this purpose, vibration data collected off a faulty system over many days is analyzed in both time and frequency domains to excavate data features related to the degradation and then identify the health indicator (HI) and the failure mechanism. At that point, the constructed HI, thanks to the feature importance ranking and principal component analysis (PCA) techniques, is used as a mathematical input for an exponential degradation model. Finally, the results are validated using real-world vibration-based degradation information leading to an effective predictive strategy for industrial systems.

Rotating machines such as induction motors are crucial parts of most industrial systems. The prognostic health management of induction motor rotors plays an essential role in increasing electrical machine reliability and safety, especially in critical industrial sectors. This paper presents a new approach for rotating machine fault prognosis under broken rotor bar failure, which involves the modeling of the failure mechanism, the health indicator construction, and the remaining useful life prediction. This approach combines signal processing techniques, inherent metrics, and principal component analysis to monitor the induction motor. Time- and frequency-domains features allowing for tracking the degradation trend of motor critical components that are extracted from torque, stator current, and speed signals. The most meaningful features are selected using inherent metrics, while two health indicators representing the degradation process of the broken rotor bar are constructed by applying the principal component analysis. The estimation of the remaining useful life is then obtained using the degradation model. The performance of the prediction results is evaluated using several criteria of prediction accuracy. A set of synthetic data collected from a degraded Simulink model of the rotor through simulations is used to validate the proposed approach. Experimental results show that using the developed prognostic methodology is a powerful strategy to improve the prognostic of induction motor degradation.

The synthesis of an internal model control (IMC) that is equable to the inverse of the model is extremely important in order to ensure perfect set-point tracking. This inversion presents the fundamental problem of the IMC structure for multivariable (MIMO) under-actuated systems with parametric uncertainty. However, the proposed IMC strategy is extended in the case of multi-input multi-output systems such that the number of control inputs is less than the outputs one. In the controller design procedure, a new method is presented to add to the model matrix underactuation a number of transfer function in order to make it a square matrix. Furthermore, the internal model control structure is changed in order to eliminate the excess control inputs by using the usual arithmetic operators. The problem of stability of controller is solved by applying a linear matrix inequality (LMI) approach, which can be easily solved using numerical standard software such as MATLAB. Robust stability of final closed control loops is tested using the value set concept and zero exclusion condition. In a set of experiments, the controlled system is approximated by a MIMO matrix with parametric uncertainty, the controllers are designed, the robust stability is verified, and the final control responses are tested and evaluated. Simulation results have proved the effectiveness and reliability of the proposed method.

This paper proposes an approach of Internal Model Control (IMC) for the linear sampled multivariable non-square systems with outputs more than inputs. The proposed IMC, which is based on a specific inversion principle, is the extension of to the case of square systems, in the controller design, a simple method is presented to add a complementary transfer function matrix of the process to obtain a square transfer matrix. To guarantee the robust stability of the system and disturbance rejection, the controller design procedure is proposed to obtain the wish control. Simulation illustrate the effectiveness of the proposed method for non-square multivariable processes.