Diagnostic & Prognostic Technology for Ground Vehicles

Contributor:  IDGA Staff
Posted:  09/26/2012  12:00:00 AM EDT
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IDGA with Jeffrey Banks, Department Head/Research Engineer, Applied Research Laboratory, The Pennsylvania State University

Explore for us your background within military and light armored vehicles.

I started my career in an industry where condition-based maintenance practices and predictive fault detection technologies have been developed and implemented widely over the last 30 years.  The paper manufacturing, nuclear power and steel manufacturing industries have leveraged this technology much broader and deeper than DOD. 

For the last 13 years I’ve worked here at the Applied Research Laboratory at Penn State University where we focused on the development and application of vehicle health monitoring, health management, and condition based maintenance applications for ships, aircraft, helicopters and military ground vehicles..


Tell us a little about the aims and the current status of ‘Diagnostics’ in general for the Army and Marine Corps Ground Systems.

The U.S. military is in the process of integrating condition based maintenance practices when it is technically and economically appropriate into their existing preventative maintenance methodology, where maintenance is conducted on a scheduled basis, such as changing the engine oil on a calendar interval regardless of the oil condition.  A condition based maintenance methodology is predicated on the utilization of sensor data to interpret the health of the platform and the need for maintenance is based on the sensor based fault detection and vehicle usage capability such as the ability to detect or determine a degraded engine oil condition.  Ideally, the oil would not be changed until a sensor indication of degradation, which is a more cost effective approach because the oil is utilized to its full potential.    

To enable the integration of condition based maintenance, there are several efforts being conducted that are focused on leveraging vehicle data from sensors that currently exist on vehicle.  Many military ground vehicles have some maintenance troubleshooting capability known as BIT, or Built-In-Test that provides a course diagnostic description but often this information is not accessible to the military maintainer because they don’t have the technology or training to utilize the information.   So the military ground vehicle community would like to not only leverage the existing diagnostic information but also data from potential additional sensors by allowing that data to be more accessible to the military maintainers with enabling information technologies.  This capability is similar to a commercial system like OnStar, which provides their customers with a monthly maintenance report with fault codes and usage information for their vehicle. In general, the military would like to bring similar capability to their maintainers.


What other condition based maintenance algorithms and models are being applied to reduce the overall cost of maintaining military vehicles?

Many organizations are developing vehicle usage models and failure prediction algorithms that will enable the ability to implement condition based maintenance.    

There are many approaches for predicting the potential failure of vehicle components but in order to determine which of these techniques will work effectively, more vehicle component failure data is needed for algorithm validation. There are also models that are being used off-platform to better understand how the vehicles are being used, where the model provides information such as, the type of terrain that the vehicle been driven on and the general driving characteristics of the operator.    Other models provide information about much load the vehicle has carried without adding a load sensor to the vehicle.  So there’s a lot of progress being made with usage and predictive modeling techniques.


Speak to how predictive maintenance serves to keep ground forces ‘mission ready’.

Going back to the capability that is currently available to the commercial market, where you can take your vehicle to a mechanic who has a diagnostic system that can read fault codes and diagnostic messages that enable the ability to conduct more effective maintenance. 

There is also the additional capability, where the OnStar product has the capability to automatically transfer data off of the vehicle through the cellular networks and analyzing the data to provide the customer with vehicle usage information, performance status and monthly maintenance service recommendations.  The report recommendations provide preventative maintenance activities that need to be done in the near future and fault codes that enable condition based maintenance that are based on vehicle sensor data and usage data such as how the vehicle is being driven.  The military is trying to move toward with this concept.  In addition, DoD is migrating to commercial based ERP systems or data management systems, so the data can be analyzed off-platform, and actionable maintenance information can be provided back to the maintainer basically as an OnStar capability does. 


So explain a little bit more about predictive maintenance and how the military reduces costs by performing work on vehicles when it’s needed, based on the conditions of the part.

The real strength of these technologies, whether it’s for DOD or commercial use is to have sensor based fault detection and prediction algorithms with diagnostic and predictive technology driven maintenance activities because it enables more cost effective asset management. 

For one, this forecasting capability enables the ability to conducted planned maintenance as opposed to unscheduled maintenance.  The military of course doesn’t want to send vehicles out on a mission if they’re going to fail.  So any predictive capability that can provide information such as, ‘the vehicle fuel pump is going to fail in the next 72 hours’ enables the ability to conduct the appropriate scheduled maintenance actions that could be conducted before that mission which are more cost effective when compared to unscheduled maintenance actions after a mission has started. 

It’s the same story in the commercial side.  Before you go on a vacation or any sort of trip, you want to know whether your vehicle is going to have problems, so the appropriate maintenance actions can be implemented before the trip. 

Predictive maintenance capability provides very useful information directly to the maintainer, but it’s also very powerful information technology for the support personnel, who have to get the replacement parts to the maintainer to repair the vehicle.  When something fails on any given system or platform, there’s the maintenance activity, which involves trouble shooting the problem, removing the damaged or failed part, and ordering a new part.  But ordering a new part and waiting for that part to arrive can take a significant amount of time, especially in a military environment where the spare parts might be on the other side of the world.  So as soon as you have a prediction of a part failing while the vehicle is still operational, let’s say with a two weeks forecast, then the vehicle can still be utilized and the maintenance action can be scheduled for when that part is going to arrive in the next week or two which reduces the operational downtime of the vehicle.

So that predictive capability is important for both the maintainer as well as the logistician that is supporting those platforms.

IDGA Staff Contributor:   IDGA Staff

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