Abstract: Multi-actuator system. The system includes at least two nano positioners having different ranges and bandwidths located in cascaded serial form to contact and move an object. A control system employs data-based control design to combine the at least two nano positioners so as to apportion actuation responsibilities among the at least two nano positioners so as to compensate for their coupled dynamics while moving the object. It is preferred to provide a separate controller for controlling separately each of the at least two nano positioners. Parameters of the separate controllers may be determined by minimizing output error.

Abstract: Multi-actuator system. The system includes at least two nano positioners having different ranges and bandwidths located in cascaded serial form to contact and move an object. A control system employs data-based control design to combine the at least two nano positioners so as to apportion actuation responsibilities among the at least two nano positioners so as to compensate for their coupled dynamics while moving the object. It is preferred to provide a separate controller for controlling separately each of the at least two nano positioners. Parameters of the separate controllers may be determined by minimizing output error.

Abstract: A parameter-free method to analyze sensor signals incorporates two or more of frequency demodulation, amplitude demodulation and phase demodulation of the raw signal data. The resulting signal is transformed to a frequency domain, and target fault characteristics from the demodulated signal are identified. The method is used to detect faults in bearings, gears and other mechanical components.

Abstract: Rheology system. The system includes a first piezoelectric actuator assembly for providing microscale displacement of a sample and a second piezoelectric actuator assembly for oscillating the sample at a nano/micro scale displacement in a selected frequency range extended significantly as compared to the frequency range available on the commercial AFMs. A preferred sample is cartilage and the disclosed system can distinguish between normal cartilage and GAG-depleted cartilage.

Abstract: A signal analysis method extracts transient target signals of known type from a raw data source signal that contains an unknown number of target signals. The method can enhance the analysis of data obtained from in-line oil-debris sensors. The method comprises steps of: defining signatures of the known target signal, and of at least one of the intrinsic noise and interfering signals; performing a mathematical transform that decomposes the raw data into distinct data sets; using the signal signatures to identify and nullify the data sets containing noise and interfering signal signatures; using the target signal signatures to identify the data sets containing target signal components, or may further use a thresholding rule to remove intrinsic noise from said data sets, and finally applying the inverse transform to the processed data sets in order to reconstruct an enhanced output signal.

Abstract: A parameter-free method to analyze sensor signals incorporates two or more of frequency demodulation, amplitude demodulation and phase demodulation of the raw signal data. The resulting signal is transformed to a frequency domain, and target fault characteristics from the demodulated signal are identified. The method is used to detect faults in bearings, gears and other mechanical components.

Abstract: A signal analysis method extracts transient target signals of known type from a raw data source signal that contains an unknown number of target signals. The method can enhance the analysis of data obtained from in-line oil-debris sensors. The method comprises steps of: defining signatures of the known target signal, and of at least one of the intrinsic noise and interfering signals; performing a mathematical transform that decomposes the raw data into distinct data sets; using the signal signatures to identify and nullify the data sets containing noise and interfering signal signatures; using the target signal signatures to identify the data sets containing target signal components, or may further use a thresholding rule to remove intrinsic noise from said data sets, and finally applying the inverse transform to the processed data sets in order to reconstruct an enhanced output signal.