Winds of Change — Predictive Faults — Tesla
Tesla recently announced that they plan to use “Stress Sensors” deployed throughout the vehicle in order to predict a failure before it occurs. This of course presents an interesting perspective for those keeping an eye on the horizon. Tesla may be an outlier here since their vehicles are a little different than most but I‘m wondering what your thoughts on this are.
Predictive fault codes could be for physical stress measured by position sensors or current monitor for components. Current monitoring of a Module controlled output could be logged and compared. Peak and continuous operating current showing an increasing trend over time may predict component failure. This will lead to more sensitive electronics requiring consistent thorough electrical testing…
Hi Scott. General Motors does something similar though not to the same level as Tesla. They have what they call, self diagnosing Proactive alerts, as part of the OnStar system. Proactive alerts are available on select 2015 to current models. Battery, starter motor and fuel delivery systems are monitored based on vehicle data and customers receive in-vehicle messages via OnStar as well e-mail
Hi Allan, Thanks for sharing that info. I was aware of the predictive battery related items that GM was performing but didn't know about the fuel system monitoring. Was that something that the no longer support or is now optional? And do you know what they were doing in regards to fuel? Were they monitoring fuel pump performance or fuel trim issues? There's some interesting predictive methods
Hi Scott, the fuel system monitoring is no longer supported. The algorithm used fuel pressure sensor performance as well as fuel pump resistance to predict performance degradation of the fuel delivery system.
Hi Scott. GM has had Proactive Alerts for some years now, beginning circa 2014 Impalas from recall. Here's a Techlink article on the topic from 2016. Starting And Charging (SAC) and Engine Controls Fuel (ECF) are still listed in SI for 2018 Cruze, but ECF has been discontinued for 2019 on the same vehicle. Models previously supported will continue to be supported, but it is important to note
Thanks for that Martin, I appreciate the intel. I’ll also try to find that Techlink article and fix your link. It appears that the link got borked in your post somehow. On the disappearance of that pid, do you suspect that was an accident or other?
Hi Scott. Sorry about the link. I probably posted using IE11 instead of Chrome that night. The link seems to be working fine now, so thanks for fixing it. As for the disappearance of the "ECF" Proactive Alert PID, I would think that it was intentional. We do notice changes from time to time and may never know why, unless we bump into the person directly responsible for making the change…
After watching several of Rich Rebuilds videos on Youtube, it's obvious that there's several failure prone devices on these vehicles. There was one video he posted, where a friend of his re-engineered the switches for the door handles, because they are prone to water intrusion, and wire breakage, because of the design. They made a new switch harness with the same switches that Tesla uses, but
It is amazing what can be modeled when there is enough data and math applied. I'm currently facinated by KIA's usage of the Knock sensor to predict crankshaft/rod bearing wear/pending failure (knock). …L P1326.
Hi Todd, If only they would advise customers to care for their vehicles properly. If they did, that algo probably would never need to be accessed.
Not just 14-15 2.4l. My wife has a 16 optima with a 2.0 turbo and they've just issued a flash update for the ecm to tighten up the knock detection. Gotta take it in this weekend. After the update, if the engine fails they will replace it for free! Unlimited time and mileage according to the paper in the mail. Maybe mine won't fail, I use the correct oil...
Depends what the "Stress Sensors" are. If it is a bunch of stuff which has to be wired back to a data collection device that can be quite costly to put into every vehicle. This is usually reserved for pre-production models and prototypes. It could very well be a mostly software only "device" which many manufacturers already do today. Getting the information phoned home and who owns it is what is
I think a lot of automotive companies have dabbled in prognostics - using data downloaded from vehicles to predict when a component or system will no longer meets it's intended function. A simple example of that is oil minder strategies that tell the customer when to change oil or looking at charging patterns to predict battery failures. I think our experience is that most customers do not want
Thanks for sharing that with us Paul, I really appreciate it. Yes, it’s easy to see one drowning in data trying to decipher what’s important and what’s not. This is where Artificial Neural Networks (ANN) come into play and they’re not going away. Kinda scary and interesting at the same time.
Hi James, While ANN’s have been around for many years now, there really hasn’t been much talk about it within our environment because of the obvious. Most of the learning for me has been through research although I was privileged recently to attend a presentation recently about ADAS, cameras and how these neural networks are ”visualizing” our environment. Back in about 2005 I learned that one
James, After chatting with Scott I decided I'm going to post a series of articles on Artifical Neural Nets/Deep Learning. I hope to keep most of the math out and just focus on past, present, and future applications in the automotive and equipment industries. Stay tuned, first one should be up in a day or two.
HI Scott, Ford had a Neural Network misfire monitor in 2005 on the 6.8L V-10, but we removed it from production that same year. It worked great until it didn't. The key to Neural Networks in the training set. You have to teach it what's good and what's bad. We trained the network on "good" engines with various misfire patterns and the NN learned everything perfectly. We finally had great monitor
Thanks for the insight Paul, I had no idea this was happening. Training a machine to think like a human is fascinating to me but I think we're quite a ways away from something as intelligent as a human, especially as it relates to human behavior. Thinking about self-driving cars, the systems today are likely unable to interpret and process what the lady 2 lanes to your left at 45 degrees will
Hello Paul, that is very interesting. I certainly understand the training aspects but what actual engine parameter inputs were used for the monitor? Was it the crankshaft monitor only? Or were there other data elements used to train and make the determination of a misfire? The OBD spec. for that has always been quite challenging. I feel for the heavy duty and diesel guys who are being asked to
Hi Robert, the way a neural network works is to take various inputs, weight them using a coefficient and feed them through a transfer function which is then used to feed other transfer functions which results in an output. The network coefficients are learned by training the network. You provide the input data and the desired output to develop the coefficients. The Ford system used acceleration
Scott, Have you looked at the way Toyota describes the learning nature of their A/C systems? Looks very familiar. I was looking at data from other makes and found multiple duct temp sensors, sun load, sun elevation and sun azimuth as just some of the PIDs and these were Electronic Manual "Single" zone systems..........
Jim, adaptive strategies are very different from neural networks. Neural networks have no adaptive capabilities - they must be trained. Adaptive strategies are widely used in powertrain control software. I can see Toyota making use of them in A/C control.
Paul, perhaps my imprecise application of language is creating some confusion. I am still learning. Toyota uses the term neural network and even has a diagram much like the article that Scott linked in the post I replied to. I obviously cannot copy and past Toyota SI here, but for anyone looking for documentation.... Go to the NCF (New Car Features) section of SI for a 2016 Tacoma. (This is
Hi Jim, In my limited research on NN’s, I recently met someone who provided my with a simplified perspective on the basic “types” of training taking place in the hidden layers which are as follows: - Reinforcement learning - telling it what the goal is and it continues to try and learn the goal - Supervised - data with expected responses - Unsupervised learning - give data and tell the
HI Jim, Thanks for the discussion. I didn't mean to imply that every single possible input needs to be in a neural network. You only have to have the important ones. You would see that show up in the data as the resulting weighting coefficients would very small for the inputs that didn't matter to the outcome. Misfire is a bit different in that all kinds of things affect crankshaft
As part of the patent phrasing I find it interesting that a reason for the stress sensors is that: "performing regular inspections may be time-consuming and costly" I think a vast majority of technicians that I speak to do not find merit in the evidence of component decay that we can monitor now, and doubt that a shift to this type of system in other manufacturers vehicles would be utilized to
I think the point that Keith brings up is valid. I also believe that Tesla knows this as well. This is one of the many reasons I think they will hold off as long as they can letting any type of diagnostic data out to the publi. I suspect in the years to come if and when it does become available they will make the shops go through rigorous training prior to being given those tools.