# Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer
###### Abstract  
This study focuses on generating reliable signals from measured noisy signals through an enhanced sensor fusion method. The main contribution of this research is the development of a novel sensor fusion architecture that creates virtual sensors, improving the system's redundancy. This architecture utilizes an input observer to estimate the system input, then it is introduced to the system model, the output of which is the virtual sensor. Then, this virtual sensor includes two filtering stages, both derived from the system's dynamics--the input observer and the system model--which effectively diminish noise in the virtual sensors. Afterwards, the same architecture includes a classical sensor fusion scheme and a voter to merge the virtual sensors with the real measured signals, enhancing the signal reliability. The effectiveness of this method is shown by applying merged signals to two distinct diagnosers: one utilizes a high-order sliding mode observer, while the other employs an innovative extension of a predefined-time observer. The findings indicate that the proposed architecture improves diagnostic results. Moreover, a three-wheel omnidirectional mobile robot equipped with noisy sensors serves as a case study, confirming the approach's efficacy in an actual noisy setting and highlighting its principal characteristics. Importantly, the diagnostic systems can manage several simultaneous actuator faults.  
sensor fusion; fault diagnosis; predefined-time diagnoser; nonlinear systems; mobile robot +
Footnote †: journal: _Algorithms_  
the system sensors may be measuring signals which are not comparable with each other, which may make merging them with a sensor fusion technique difficult. To avoid this last drawback, in the literature changing the sensor coordinates has been proposed [12; 13]; regrettably this strategy is limited to very particular cases.  
This work proposes a novel sensor fusion architecture capable of generating several virtual sensors comparable with each other. These are merged in a sensor fusion stage to obtain more reliable output signals for subsequent processes, such as fault diagnosis, observers, and controllers. The idea behind the proposed architecture is to include two filter stages that generate feasible system inputs and outputs based on the system model, significantly reducing signal noise. Additionally, a voter enhances the reliability of virtual sensor signals by selecting those with the minimum variance.  
To show the efficiency of the proposed architecture, its output is used by two diagnosers, one based on a high-order sliding mode (HOSM) observer, and the other based on an enhanced predefined-time observer herein introduced. Both cases show that when the proposed architecture is used, the efficiency of the diagnosers is improved, i.e., the false positive/negative fault detections are reduced, and the fault diagnosis is improved.  
This work is organized as follows. Section 2 introduces the sensor fusion architecture, emphasizing how from a subset of output signals and using an input observer scheme, the virtual sensors may be implemented. This section also introduces a novel diagnoser based on an enhanced predefined-time observer. Section 3 introduces a three-wheeled omnidirectional mobile robot, that is the real system on which the proposed architecture and diagnosers are tested. Section 4 shows the experiments and the derived results. All of them show that the use of the proposed architecture improves the diagnosis of faults. Finally, the conclusions are presented.