Abstract: A Stall Warning System (SWS) utilizing the detection and analysis of stress waves, (i.e., ultrasound) emitted by combustion and/or turbulent flow processes of an apparatus, such as a turbine engine, for example. Upon detection of an impending stall condition, the SWS can inform the operator, inform another electronic device (computer, etc.) and/or latch the event for review by maintenance personnel.

Abstract: A statistical process, and system for implementing the process, is described for the analysis of stress waves generated in operating machinery or equipment. This technique is called Probabilistic Stress Wave Analysis. The process is applied to a population of individual “feature” values extracted from a digitized time waveform (such as a 2 second Stress Wave Pulse Train, or a 2 month history of Stress Wave Energy). Certain numeric descriptors of the statistical distributions of computed features are then employed as inputs to decision making routines (such as neural networks or simple threshold testing) to accurately classify the condition represented by the original time waveform data, and thereby determine a status of the operating machine/equipment.

Abstract: A statistical process, and system for implementing the process, is described for the analysis of stress waves generated in operating machinery or equipment. This technique is called Probabilistic Stress Wave Analysis. The process is applied to a population of individual “feature” values extracted from a digitized time waveform (such as a 2 second Stress Wave Pulse Train, or a 2 month history of Stress Wave Energy). Certain numeric descriptors of the statistical distributions of computed features are then employed as inputs to decision making routines (such as neural networks or simple threshold testing) to accurately classify the condition represented by the original time waveform data, and thereby determine a status of the operating machine/equipment.

Abstract: A Stall Warning System (SWS) utilizing the detection and analysis of stress waves, (i.e., ultrasound) emitted by combustion and/or turbulent flow processes of an apparatus, such as a turbine engine, for example. Upon detection of an impending stall condition, the SWS can inform the operator, inform another electronic device (computer, etc.) and/or latch the event for review by maintenance personnel.

Abstract: A sensor is disclosed specifically for detecting stress waves for use in a stress wave analysis system. The stress waves-are preferably detected in a narrow frequency range of 35-40KHz. At this range, stress waves from friction and impact sources typically propagate through machine structures at detectable amplitudes. In order to maximize the signal to noise ratio of stress waves, relative to background noise and vibration, the sensor of the present invention is designed and calibrated with a frequency response and damping features that are specifically tailored for stress wave analysis.

Abstract: A sensor for detecting stress waves for use in a stress wave analysis system. The stress waves are preferably detected in a narrow frequency range of 35-40 KHz. At this range, stress waves from friction and impact sources typically propagate through machine structures at detectable amplitudes. In order to maximize the signal to noise ratio of stress waves, relative to background noise and vibration, the sensor of the present invention is designed and calibrated with a frequency response and damping features that are specifically tailored for stress wave analysis. The sensor is a multi-functional sensor that can measure a number of logically related parameters for indicting the mechanical condition of a machine. It is often desirable to measure both friction and one or more other parameters appropriate for indication of a machine's health, where all of the measuring capability is contained in one sensor.

Abstract: A method for stimulating a sensor and measuring it's output over a frequency ranges starting at a frequency f1 and ending at a frequency f2 is disclosed, which can include the following steps: (a) placing an assembled sensor on a shaker table; (b) setting the shaker table at a specified frequency and exciting the sensor by moving the sensor up and down at an amplitude at least approximately equal to a reference “g” level of acceleration; (c) measuring the sensor's output and recording the measured output as a first value; (d) incrementally changing an excitation frequency of the shaker table and adjusting the amplitude to achieve the reference “g” level; (e) measuring the sensor's output and recording the measured output as a second value; and (f) repeating steps (d) and (e) for one or more discrete frequencies within the frequency range to provide a frequency response curve for the sensor over the frequency range.

Abstract: A Foreign Object Damage (FOD) detection system is disclosed for detecting and analyzing ultrasound or stress waves emitted when an object enters the intake of a turbine engine and impacts one or more of the blades in the engine. Upon detection the FOD detection system can immediately inform the operator, inform another electronic device (computer, etc.) and/or latch the event for review by maintenance personnel. The detection system generally consists of one or more stress wave sensors and an electronic assembly to process the stress wave signal received from the sensor(s). The electronic assembly is in communication with the sensor(s) via conventional cabling.

Abstract: A sensor is disclosed specifically for detecting stress waves for use in a stress wave analysis system. The stress waves are preferably detected in a narrow frequency range of 35-40 KHz. At this range, stress waves from friction and impact sources typically propagate through machine structures at detectable amplitudes. In order to maximize the signal to noise ratio of stress waves, relative to background noise and vibration, the sensor of the present invention is designed and calibrated with a frequency response and damping features that are specifically tailored for stress wave analysis.

Abstract: A sensor for detecting stress waves for use in a stress wave analysis system. The stress waves are preferably detected in a narrow frequency range of 35-40 KHz. At this range, stress waves from friction and impact sources typically propagate through machine structures at detectable amplitudes. In order to maximize the signal to noise ratio of stress waves, relative to background noise and vibration, the sensor of the present invention is designed and calibrated with a frequency response and damping features that are specifically tailored for stress wave analysis. The sensor is a multi-functional sensor that can measure a number of logically related parameters for indicting the mechanical condition of a machine. It is often desirable to measure both friction and one or more other parameters appropriate for indication of a machine's health, where all of the measuring capability is contained in one sensor.

Abstract: A distributed stress wave analysis system is disclosed for detecting structure borne sounds cause by friction. The detected information is processed using feature extraction and neural network artificial intelligence software. The system consists of stress wave sensors, interconnect cables, and preferably three modules: (1) distributed processing units, (2) maintenance advisory panel, and (3) laptop computer. A derived stress wave pulse train which is independent of background levels of vibration and audible noise is used to extract signature features, which when processed by neural networks of polynomial equations, characterize the mechanical health of the monitored components. The system includes an adjustable data fusion architecture to optimize indication thresholds, maximize fault detection probability, and minimize false alarms.

Abstract: For automatically predicting machine failure a transducer sensor, such as piezoelectric crystal, is applied to a machine for sensing machine motion and structure-borne sound, including vibration friction, and shock waves. The structure-borne sound and motion sensed is converted to electrical signals which are filtered to leave only the friction and shock waves, which waves are processed, as by detecting the envelope and integrating beneath the envelope, resulting in a measure of friction and shock wave energy, i.e., stress wave energy. This measure is computed and processed for producing fault progression displays for periodic and aperiodic damage. This is accomplished in a personal computer, menu-driven environment.

Abstract: For use in predicting machine failure a transducer sensor, such as piezoelectric crystal, is applied to a machine for sensing machine motion and structure borne sound, including vibration friction, and shock waves. The structure borne sound and motion sensed is converted to electrical signals which are filtered to leave only the friction and shock waves, which waves are processed, as by detecting the envelope and integrating beneath the envelope, resulting in a measure of friction and shock wave energy. This measure may be compared with that of a less used machine, say the same machine when new, to indicate the rise in energy due to change in machine parts.