“How much longer will it last?” is a common question that motorists in for repairs ask service technicians. And although a “best guess” may be fine for deciding when to replace tires and brakes, other vehicle parts and systems raise more time-sensitive questions. “Will the car start the next time I turn the key?” is an example, and that’s where prognostics—predicting remaining service life of systems and components—is becoming a technical area of increasing interest. Panelists took a fresh look at a 2015 SAE World Congress panel led by Tim Cavanaugh, Global Telematics, at Delphi, using a new General Motors vehicle health monitoring system as the springboard.
Focus on starting system parts
Electronic modules have been keeping a close watch on the battery packs and control electronics of electric vehicles and plug-in hybrids since their introduction, because battery capacity and vehicle range are important. Any deterioration is of great concern, particularly for a motorist expecting to make trips of a specific distance on a regular basis.
Heavy-duty vehicle fleets also are in the picture, for as panelist Bernie Porter, Manager of Powertrain Calibration and Controls for Mahle Powertrain noted, “they don’t want a piece of machinery to end up sitting in the middle of nowhere.” Of course, the aviation industry has a long-established focus in this area, as part of required maintenance, seeking to balance safety with cost-effectiveness.
Although Porter said “it’s harder to make a business case” for a major investment in a car, the issue of customer satisfaction was noted by Steven W. Holland, GM Research Fellow for Integrated Vehicle Health Management.
He told attendees that the initial GM entry into prognostics for conventional passenger cars is focused on the battery, starter, and fuel pump, because these are the key components that must be healthy for the car to start. The “positive” nature of the prognostics takes the Vehicle Health Report a giant step past what many motorists consider a sales pitch for oil changes and similar maintenance items.
The GM prognostics approach is customer-centric, Holland said, and added that it’s not really quality but reliability that the customer perceives. If the predictive nature of the software can become 90% accurate in determining when a component falls out of spec, that would greatly enhance the impression of reliability. The motorist who can tell his/her friend that the battery was replaced free even though the car was starting and running fine, would be a salesman for prognostics.
4G connection required
The parts or systems may have to be built to a specific design because the prognostics algorithm needs information that might not necessarily be routinely available so that a decision to call for replacement is made with high confidence.
For GM’s initial foray, the battery is not a special design, although the algorithms vary according to vehicle, and perhaps one AGM (absorbent glass mat) battery or two conventional batteries could be used (instead of one) for a stop-restart system. For the most part, Holland said, the motorist should not experience a loss in effectiveness of the battery health assessment with a replacement battery. Any specific requirements, such as a battery temperature sensor, would be external, and any voltage/amperage monitoring would be in place for diagnostics of electrical circuits.
An important aspect of the algorithm is that there’s detailed information on the battery, starter, and fuel pump—and other systems—every time the motorist starts the car, not just the cranking voltage drop and the time to start. That information is transmitted by the OnStar 4G connection in the cars that have the prognostics feature. Without the ability to get information on demand via 4G, there would be no practical way to deploy the algorithm.
The vehicle health algorithms are not based on trouble codes logged or even some obvious deterioration. The readings a technician takes would look absolutely normal, Holland said. But the sophistication in the prognosis is based on changes in readings that indicate an impending problem.
Fuel pump example
Holland pointed to the electric fuel pump as an example. When new, the pump can deliver a lot more pressure and fuel volume than is needed. So when the algorithm sees numbers that still are within the acceptable range, but are declining at a rate beyond expectations, the monitoring will become more intensive. The algorithm will look for signs of an impending effect on hard acceleration and high loading, but before the driver is likely to notice anything, he said.
Some of the information also comes from “hidden code,” which are algorithms that are in the software but not in any service diagnostics. These algorithms were installed (and never removed) as part of the calibration development process and now have been found to help make some prognostics decisions.
Holland said that when a GM health report calls for battery replacement under warranty, the health report is so robust that “there’s no risk to GM because we’d eventually be paying for it anyway. If we didn’t, we’d just have an upset customer.”
He noted there is an overall challenge posed by all the electronic components from suppliers, who have proprietary code baked in. “We need the indicators of health” from that code, he said.
Some session attendees expressed concern over the possibility that the prognostics decisions, transmitted to the car dealer, would shut out the aftermarket. However, others pointed out that the “vehicle health report” would go to the motorist and his designated “preferred service provider,” which could well be an independent garage.
To minimize the effect on the vehicle’s 4G data plan, the prognostic algorithms are prudent users. As Delphi’s Cavanaugh said, “you don’t want to grab everything. You want to reduce the data requests to just when and what you need.”
Diagnostics algorithms on cloud server
Diagnostics/prognostics information is transmitted to GM’s cloud server, which eliminates the need for onboard algorithms that not only would take computer capacity, but also require continuous data bus reprogramming to maintain currency. The prognostics reports are strictly voluntary for the motorist, Holland explained. GM provides the telematics connection for five years “if they want it. If they say no, we don’t enable it.”
Holland distinguished the prognostics analysis from anything that turns on a Check Engine light. He pointed out that “the government says we must have it and light it when certain things happen.” There’s no difference, he added, between something that’s three months to a needed service, something right now, or even the loose gas cap. “So the motorist has become trained to ignore it (the light) when there’s no obvious problem.”
Prognostics also has a future in predicting future life of such electromechanical systems as automatic transmissions. They already have sensors that read hydraulic line pressure, input and output shaft rpm, and clutch engagements, so predictive capabilities are there.
The future also could include ways to detect impending failure of gears and bearings, explained Mohamed El-Morsy, of the Czech Technical University of Prague. He described laboratory testing of a five-speed manual transmission that had a pitting fault introduced on a small part of the surface area of a gear tooth. It was subject to a specific wavelet filter (Morlet) with a statistical measure (Kurtosis) of the number and amplitude of peaks in a vibration signal. Taking the laboratory work into an in-car transmission requires a sensor of course, and El-Morsy said the university is working with Skoda to develop one.