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Prognostics [clear filter]
Wednesday, May 15
 

1:30pm

Design Approach for a PHM System: Dual Set of Electromechanical Actuator Subsystems
This paper describes a design for an example of a robust Prognostic Health Monitoring (PHM) system for a dual set of electromechanical actuators (EMA) subsystems. The paper is presented as a case study to address some of the major in a design of a PHM system that you face. Given a set of assemblies and subassemblies that are to be monitored for prognostic support, (1) How do you continually and simultaneously monitor them? (2) What considerations are related to sampling rates; prediction accuracy, resolution, and precision; state-of-health (SoH); (3) How do you associate a node or nodes to which one or more sensors are attached to software-program modules to condition data and extract features? (4) Is data to be acquired and processed in real time or near real time (directly from a sensor to program modules to prediction algorithms) or as batched data kept in files? The paper uses historical and experimental data with results from an example PHM system.

Speakers
avatar for James Hofmeister

James Hofmeister

Distinguished Engineer, Ridgetop Group, Inc.


Wednesday May 15, 2019 1:30pm - 2:00pm
Freedom Ballroom I

2:00pm

Evaluating the Accuracy of Prognostic Estimates: Remaining Useful Life, State of Health, Prognostic Horizon
This paper describes how to evaluate the accuracy of prognostic estimates for remaining-useful life (RUL), state of health (SoH), and prognostic horizon (PH). The primary goal of prognostics is to be able to accurately predict a future failure in systems, including condition-based-maintenance (CBM) systems that use condition-based data (CBD) to detect degradation and project that degradation to a failing level at a future time. That future time and the time when the data is sampled are used to calculate predictive (prognostic) information such as RUL, SoH, and PH. Evaluating the accuracy of prognostic information is critical and begins with knowing that ideal, zero-error estimates are not practical, primarily of factors such as the following: how long it takes for degradation to progress to a failing level, when the onset of degradation occurs, changes in rate of degradation due to operating and environment conditions, and noise in the sensor-measurement system. Performance metrics to evaluate accuracy are developed and presented: convergence efficiency, prognostic distance (PD), and prognostic-horizon accuracy (PH).

Speakers
avatar for James Hofmeister

James Hofmeister

Distinguished Engineer, Ridgetop Group, Inc.


Wednesday May 15, 2019 2:00pm - 2:30pm
Freedom Ballroom I

2:30pm

How To Initiate Sensor Fusion within a Digital Environment
Machinery diagnostics is entering a new era, with new urgency, as industry moves toward better asset management and eventually to unmanned operations. Owners and operators are expecting the advanced machines of the future to have the ability to self-diagnose conditions that could lead to catastrophic failure or to unanticipated down time. Next-level, algorithm-driven associations to yield, machine efficiency, and other operating characteristics that can be defined in terms of the energy associated with known machine processes can also be translated into useful parameters for transmission over a digital data bus.

To reach those goals, Dytran has created CAN-MD®, a cutting edge, digital sensor platform that processes raw analog data inside the sensor, enabling a bussed architecture that delivers actionable results, not raw data. Advanced sensors with on-board digital signal processing (DSP) features are the key to this new machine awareness. The advent of smaller, more powerful microprocessors enables a new generation of bus-based digital vibration sensors to process and reduce analog data inside the sensor itself.

The new technology eliminates the long wire runs to each sensor commonly associated with traditional analog test cell arrangements and replaces it with a single-cable, all-digital bus-based schema. In addition, the improved system architecture provides reduced SWaP (size, weight & power) of traditional onboard VHM (vibration health monitoring) systems, easier troubleshooting and more importantly, distributed processing.

CAN-MD® offers a variety of analog sensor adapters that allow users to add existing sensors to the CAN-MD® network. This extends many of the benefits of the CAN-MD® technology to legacy vibration sensors or other measurement node types. By expanding the measurement input possibilities, it allows the system to provide improved sensor fusion, pulling data from a greater number of nodes to allow users to make data driven decisions based on multiple sensor locations or measurements types. Sensor adapters currently include tachometers, optical blade trackers, IEPE sensors (acceleration, pressure, force) and high temperature charge mode sensors.

Our talk will discuss the digital sensor fusion domain of CAN-MD® technology and our strategy of converting all sensors to a common, bus-based environment.

Speakers
DC

Dave Change

VP, CTO, Dytran Instruments


Wednesday May 15, 2019 2:30pm - 3:00pm
Freedom Ballroom I