Publications

This paper describes a novel power usage metric which is designed to accurately track the usage of rotorcraft power train transmissions. This power usage metric is a model for defining the relationship between input power and a Power Usage Factor (PUF) that for example increases from 0 at zero input power to 1 when the main rotor transmission is operated at 100% Normal Rated Power (NRP). The results of this process are recorded and summed over time to report the accrued mechanical usage as Power Usage Hours (PUH). This power usage product represents the hours of operations that are equivalent to operating hours conducted at 100% NRP. This power usage metric is more accurate than the traditional approach that uses flight time for scheduling the overhaul of rotorcraft transmissions. In addition the functionality is an affordable addition to HUMS installations.

Condition-based maintenance (CBM) of naval assets is preferred over scheduled maintenance because CBM provides a window into the future of each asset’s performance, and recommends/schedules service only when needed. In practice, the asset’s condition indicators must be reduced, transmitted (off-ship), and mined using shore-based predictive analytics. Real-Time Innovations (RTI), Inc. in collaboration with the University of South Carolina CBM Center is developing a comprehensive, multidisciplinary technology platform for advanced predictive analytics for the Navy’s mechanical, electrical, and IT assets on-board ships. RTI is developing an open, extensible, data-centric bus architecture to integrate shipboard asset monitoring data with shore-based predictive analysis tools. The interoperability challenge is addressed using the Model-Driven Architecture (MDA) by transforming sensor data to rigorously specified standard data models. Our MDA process includes open standards such as the OMG Data Distribution Service (DDS) and Open System Architecture for Condition-Based Maintenance (OSA-CBM), both of which have enjoyed success in the Navy. Furthermore, the Navy’s Information Assurance (IA) requirements are implemented using the OMG Secure-DDS standard. In summary, the technology will improve combat readiness using a truly interoperable data-bus for exchanging CBM data from ship-to-shore while reducing distractions to the sailors, standby inventory requirements, and decision time for analysts.

In this paper, bispectral analysis of vibration signals is used to assess health conditions of different rotatingcomponents in an AH-64D helicopter tail rotor drive train. First, cross-bispectral analysis is used to investigate drive-shaft faulted conditions -- namely misalignment, imbalance, and a combination of misalignment and imbalance -- with respect to a baseline case. The magnitude of the cross-bispectrum shows high sensitivity to abnormalities in the drive shaft, and phase information can be used to distinguish between different shaft conditions. Auto-bispectral analysis is used to study vibration signals collected from a faulted hanger bearing with simultaneous drive shaft misalignment and imbalance. In the presence of drive-shaft faults, shaft harmonics dominate the power spectrum of the vibration signals, making it hard to detect the bearing’s fault using only the power spectrum. Application of bispectral analysis provides information about the fault’s characteristic frequency and relates spectral contents in the vibration to their physical root causes.

Mission benefits are seen as an important indicator of overall effectiveness of Condition-Based Maintenance (CBM) implementation. This creates incentives for Army personnel at all levels to adopt CBM. The research approach presents a step in the direction of better understanding of how mission benefit areas like morale, sense of safety etc. are influenced by perspective of army personnel who fly and maintain Blackhawk, Kiowa, Chinook and Apache helicopters around the world equipped with Health and Usage Monitoring Systems (HUMS). The study also investigates if this attitude can be described by a linear regression model. Response data collected from seventy-six helicopter personnel was analyzed to determine whether regression analysis can describe the users’ attitude towards different aspects of benefits. When cross validation was performed, the multiple linear regression model was able to predict performance response with a correlation coefficient as high as 0.95.

An important part of Condition-Based Maintenance (CBM) is the component testing of faulted articles. The University of South Carolina’s CBM test facilities have accumulated thousands of hours of component testing of faulted articles and as a result, gained invaluable testing experience. Faulted articles can fall into two categories: seeded and natural faults. Each has their benefits and drawbacks. Component testing of faulted articles can serve multiple purposes such as verifying or improving existing condition indicators or creating new ones. Faulted articles undergo a tear down analysis after testing in order to determine the actual condition of components. Three case studies are presented showing seeded fault and natural faulted testing with different drive train components. Experience shows that naturally faulted articles add significant value to CBM practices since they are closely related to actual component failures in the field. The use of seeded faults can be informative but experience has shown that it is difficult to choose an appropriate seeded fault to represent the desired failure mode. As such, care needs to be taken to choose seeded faults that have the necessary fidelity to meet test objectives.

Condition-based maintenance (CBM) is a valuable tool to any industry looking to improve productivity, product quality and overall effectiveness of critical systems. Based on the University of South Carolina’s (USC) experience and research partnerships, USC’s vision of a smart predictive system involves a comprehensive integrated methodology that involves a two prong approach: i) CBM analysis involving gathering experimental data from mechanical systems on test stands; ii) theoretical analysis involving modeling and simulation. A theoretical framework based on component, subsystem, and integrated system models is presented to understand the physics of the failure modes. The two approaches provide complementary information that can be used to enhance CBM. The correlation of these two data fusion approaches will allow the implementation of predictive tools to capture the condition of a component, subsystem and system to maximize useful life and minimize cost and risk. In addition, the predictive tools can be used to inform the next generation of system design.