A Fuzzy Logic Based Piezoresistive/Piezoelectric Fusion Algorithm for Carbon Nanocomposite Wide Band Strain Sensor

• Published in: IEEE access 2021, Vol.9, p.14752-14764
• Main Authors: Alotaibi, Ahmed, Anwar, Sohel
• Format: Article
• Language: English
• Subjects: Algorithms; Carbon fiber reinforced plastics; Clustering; Dynamic loads; Error analysis; Frequencies; frequency band; Frequency measurement; Frequency ranges; fusion; Fuzzy logic; Multi wall carbon nanotubes; nanocomposite; Nanocomposites; Optimization; piezoelectric; Piezoelectricity; Piezoresistance; piezoresistive; Polymers; Polyvinylidene fluorides; PPF; Robot sensing systems; Sensors; Strain; Strain measurement; strain sensor; wide band
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3049081

Polymer nanocomposites (PNC) have a great potential for in-situ strain sensing applications in both static and dynamic loading scenarios. These PNCs, having a polymer matrix of polyvinylidene fluoride (PVDF) with a conductive filler of multi-walled carbon nanotubes (MWCNT), have both piezoelectric and piezoresistive characteristics. Generally, this composite would accurately measure either low frequency dynamic strain using piezoresistive characteristic or high frequency dynamic strains using piezoelectric characteristics of the MWCNT/PVDF film sensor. This limits the frequency bands of the strain sensor to either piezoresistive or piezoelectric ranges. In this study, a novel weighted fusion technique, called piezoresistive/piezoelectric fusion (PPF), is proposed to combine both piezoresistive and piezoelectric characteristics to capture wide frequency bands of strain measurements in real time. This fuzzy logic (FL) based method combines the salient features (i.e. piezoresistive and piezoelectric) of the nanocomposite sensor via reasonably accurate models to extend the frequency range over a wider band. The FL determines the weight of each signal based on the error between the estimate and actual measurements. These weights indicate the contribution of each signal to the final fused measurement. The fuzzy inference system (FIS) was developed using both optimization and data clustering techniques. In addition, type-2 FIS was utilized to overcome the model's uncertainty limitations. The developed PPF methods were verified with experimental data at different dynamic frequencies that were obtained from existing literature. The fused measurements of the MWCNT/PVDF were found to correlate very well with the actual strain and a high degree of accuracy was achieved by the subtractive clustering PPF's FISs algorithm.