Kenneth P. Maynard

Enhancement of physics-of-failure prognostic models with system level features

To truly optimize the deployment of DoD assets, there exists a fundamental need for predictive tools that can reliably estimate the current and reasonably predict the future capacity of complex systems. Prognosis, as in all true predictions, has inherent uncertainty, which has been treated through probabilistic modeling approaches. The novelty in the current prognostic tool development is that predictions are made through the fusion of stochastic physics-of-failure models, relevant system or component level health monitoring data and various inspection results. Regardless of the fidelity of a prognostic model or the quantity and quality of the seeded fault or run-to-failure data, these models should be adaptable based on system health features such as vibration, temperature, and oil analysis. The inherent uncertainties and variability in material capacity and localized environmental conditions, as well as the realization that complex physics-of-failure understanding will always possess some uncertainty, all contribute to the stochastic nature of prognostic modeling. However, accuracy can be improved by creating a prognostic architecture instilled with the ability to account for unexpected damage events, fuse with diagnostic results, and statistically calibrate predictions based on inspection information and real-time system level features. In this paper, the aforementioned process is discussed and implemented first on controlled failures of single spur gear teeth and then on a helical gear contained within a drivetrain system. The stochastic, physics-of-failure models developed are validated with transitional run-to-failure data developed at Penn State ARL. Future work involves applying the advanced prognostics process to helicopter gearboxes.

Application of sensor fusion and signal classification techniques in a distributed machinery condition monitoring system

A new paradigm for machinery maintenance is emerging as preventive maintenance strategies are being replaced by condition-based maintenance. In condition-based maintenance, machinery is repaired or serviced only when an intelligent monitoring system indicates that the system cannot fulfill mission requirements. The implementation of such systems requires a combination of sensor data fusion, feature extraction, classification, and prediction algorithms. In addition, new system architectures are being developed to facilitate the reduction of wide bandwidth sensor data to concise predictions of ability of the system to complete its current mission or future missions. This paper describes the system architecture, data fusion, and classification algorithms employed in a distributed, wireless bearing and gear health monitoring system. The role and integration of prognostic algorithms--required to predict future system health--are also discussed. Examples are provided which illustrate the application of the system architecture and algorithms to data collected on a machinery diagnostics test bed at the Applied Research Laboratory at The Pennsylvania State University.

Using torsional vibration analysis as a synergistic method for crack detection in rotating equipment

A non-intrusive torsional vibration method for monitoring and tracking small changes in crack growth of reactor coolant pump shafts is presented in this paper. This method resolves and tracks characteristic changes in the natural torsional vibration frequencies that are associated with shaft crack propagation. The focus of this effort is to develop and apply the torsional vibration shaft cracking monitoring technique on a Westinghouse 93A reactor coolant pump. While this technique is being applied to reactor coolant pumps, it is generally applicable to many types of rotating equipment, including centrifugal charging pumps, condensate and feed water pumps, and may be used to detect and track changes in blade natural frequencies in gas or steam turbines. A laboratory scale rotor test bed was developed to investigate shaft cracking detection techniques under controlled conditions. The test bed provides a mechanism to evaluate sensing technologies and algorithm development. For accurate knowledge of the crack characteristics (crack depth and front), a sample shaft was seeded with a crack that was propagated using a three-point bending process. Following each crack growth step, the specimen was evaluated using ultrasonic inspection techniques for crack characterization. After inspection, the shaft was inserted in the rotor test bed for analysis and to track changes in shaft torsional vibration features. The torsional vibration measurement method has demonstrated the ability to reliably detect changes in the first natural shaft frequency in the range of 0.1 to 0.2 Hz. This technique shows the potential to enable online structural health diagnostics and ultimately the prevention of shaft or even possibly blade failure due to crack growth.

Blade and Shaft Crack Detection Using Torsional Vibration Measurements Part 1: Feasibility Studies

The primary goal of the development project was to demonstrate the feasibility of detecting changes in blade natural frequencies (such as those associated with a blade crack) on a turbine using non-contact, non-intrusive measurement methods. The approach was to set up a small experimental apparatus, develop a torsional vibration detection system, and maximize the dynamic range and the signal to noise ratio. The results of the testing and analysis clearly demonstrated the feasibility of using torsional vibration to detect the change in natural frequency of a blade due to a change in stiffness such as those associated with a blade crack.

Blade and Shaft Crack Detection Using Torsional Vibration Measurements Part 3: Field Application Demonstrations

The primary goal of the this paper is to summarize field demonstrations of the feasibility of detecting changes in blade and shaft natural frequencies (such as those associated with a blade or shaft crack) on operating machinery using non-contact, non- intrusive measurement methods. Laboratory demonstration of feasibility and some special signal processing issues were addressed in Parts 1 and 2 [1, 2]. Part 3 primarily addresses the results of application of this non-intrusive torsional vibration sensing to: a large wind tunnel fan; a jet engine high-pressure disk; a hydro station turbine; and to a large coal- fired power plant induced-draft (ID) fan motors. During the operation of rotating equipment, torsional natural frequencies are excited by turbulence, friction, and other random forces. Laboratory testing was conducted to affirm the potential of this method for diagnostics and prognostics of blade and shafting systems. Field installation at the NASA Ames National Full-Scale Aerodynamic Facility (NFAC) reaffirmed the ability to detect both shaft and blade modes. Installation on a high-pressure (HP) disk in a jet engine test cell at General Electric Aircraft Engines demonstrated that the fundamental mode of the turbine blades was clearly visible during operation. Field installation at a hydro power station demonstrated that the first few shaft natural frequencies were visible, and correlated well with finite element results. Finally, field installation on the ID fan motors also showed the first few shaft torsional modes. These field tests have resulted in high confidence in the feasibility of the application of this technique for diagnosing and tracking shaft and blade cracks in operating machinery.

Blade and Shaft Crack Detection Using Torsional Vibration Measurements Part 2: Resampling to Improve Effective Dynamic Range

The primary goal of the development project was to demonstrate the feasibility of detecting changes in blade bending natural frequencies (such as those associated with a blade crack) on a turbine using non-contact, non-intrusive measurement methods. The approach was to set up a small experimental apparatus, develop a torsional vibration detection system, and maximize the dynamic range and the signal to noise ratio. The results of the testing and analysis clearly demonstrated the feasibility of using torsional vibration to detect the change in natural frequency of a blade due to a change in stiffness such as those associated with a blade crack. However, it was found that harmonics of shaft operating speed, created as an unwanted artifact of the measurement method, resulted in spectral regions in which the effective dynamic range was inadequate to detect low-level torsional vibration associated with the natural frequencies. The loss of effective dynamic range was due to the “skirts” created by the sampling window. Application of order resampling, followed by frequency resampling, to the torsional vibration waveform increased the effective dynamic range and improved the ability to identify shaft torsional and blade bending natural frequencies.

A Non-Intrusive Technique f or On-Line Shaft Crack Detection a nd Tracking

A non-intrusive torsional vibration method for monitoring and tracking small changes in crack growth of shafts is presented in this paper. This method resolves and tracks characteristic changes in the natural torsional vibration frequencies that are associated with shaft crack propagation. While this technique is being applied to reactor coolant pumps (RCPs) it is generally applicable to any type of rotating equipment, including drivelines, and can be applied to detecting and tracking changes in blade natural frequencies in gas or steam turbines. This technique was first developed on a laboratory scale rotor test bed to investigate shaft cracking detection techniques under controlled conditions. The test bed provided a mechanism to evaluate sensing technologies and algorithm development. For accurate knowledge of the crack characteristics (crack depth and front), a shaft was seeded with a crack which was then propagated using a three-point bending process. Following each crack growth step, the specimen was evaluated using ultrasonic inspection techniques for crack characterization. After inspection, the shaft was inserted in the rotor test bed for analysis of shaft torsional vibration features. Following success in detecting and tracking crack growth on the test bed, this process was then take to a much bigger machine for verification. In the summer of 2004, the Applied Research Laboratory, along with other EPRI team members (Southern Co. and Jeumont Industrie), instrumented a 41% scale reactor coolant pump in Jeumont, France. On this platform, the team successfully detected and tracked a seeded cut through the shaft. The torsional vibration measurement method has demonstrated the ability to reliably detect changes in the first natural shaft frequency in the range of 0.1 to 0.2 Hz. This technique shows the potential to enable online structural health diagnostics and ultimately prevent shaft or even possibly blade failure due to crack growth

Gas Turbine Blade and Disk Crack Detection Using Torsional Vibration Monitoring

The primary goal of the this paper is to summarize field demonstrations of the feasibility of detecting changes in blade natural frequencies (such as those associated with a blade or disk crack) on operating turbines using non-contact, nonintrusive measurement methods. This paper primarily addresses the results of application of this nonintrusive torsional vibration sensing to a large wind tunnel fan and a jet engine high-pressure disk. During the operation of rotating equipment, blade natural frequencies are excited by turbulence, friction, and other random forces. These frequencies couple with shaft torsional natural frequencies, which may then be measured. Laboratory testing was conducted to affirm the potential of this method for diagnostics and prognostics of blade and shafting systems. Field installation at the NASA Ames National Full-Scale Aerodynamic Facility (NFAC) reaffirmed the ability to detect both shaft and blade modes. Installation on a high-pressure (HP) disk in a jet engine test cell the manufacturer’s facilities demonstrated that the fundamental mode of the turbine blades was clearly visible during operation. The results of these field tests have resulted in high confidence that this technique is practical for diagnosing and tracking blade and disk cracks.