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| sound analyzer for automating quality checks? |
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| MasterT:
I did with a psychiatrist, they told me it's " schizo paranoia ". This liberates me to disclose any confidential files, that well above your clearance, Parrot. |
| engineheat:
--- Quote from: rhb on January 17, 2019, 07:22:38 pm --- There are three sensible approaches to the problem: 1) Subtract an average ambient noise spectrum from the result. That works OK if the noise characteristics are time invariant. --- End quote --- Thanks, this is what I was thinking about as well, will give it a try. The test station is in an enclosure where it's mostly isolated from outside noise. However, inside the same enclosure, there is another mechanism down the line that creates noise. However, the noise for the other mechanism is predictable and is "in sync" with my recording interval. So for example, after 1 second into my recording, the same noise (mechanism) will occur. I guess averaging the noise FFT across the recording length and subtracting it should work decent in this case right? As a 2nd question, when we think of "decibels" we think in terms of loudness of sound without regard to frequencies. So when they said a jet engine is at certain decibel, they are not breaking it down to frequencies. So what is a correct way to interpret decibel? The total power of the FFT spectrum added together? Are there any device that can measure the "decibel" of a sound and output a single number? Thanks |
| rhb:
Sound level meters have weighting functions which are designed to match the sensitivity of the human ear. For example, "A" weighting is a common choice. so the single number is a weighted average. That's not appropriate for this problem. In your case taking the log of the amplitude at each frequency will prevent a few strong frequencies from using up all your dynamic range. You'll have to experiment at this point. I'd start by taking a long recording of the noise, breaking it up into say 1000 segments the same length as you are using to check the motors and use that. While you're at it, in addition to the mean at each frequency, compute the standard deviation. To start with I would not do the subtraction. I'd plot the mean and upper and lower 1st and 2nd sigma for the noise data and the same for a long recording of a "golden" reference motor assembly with the noise present. That way, if the noise goes outside the normal bounds, it will alert the operator. If a sample goes outside the 2nd sigma bounds on the golden reference you've got a problem motor. I'd test this for a while in parallel with the existing line operation to get a feel for the issues that might arise. While this is not very sophisticated relative to the other solutions I mentioned, it's also not trivial. You are very likely to get some surprises. Once you have data and can make plots with gnuplot, please post them so I can look at them, I don't know your age or circumstances, so I can't say whether doing an MS or PhD in industrial engineering makes sense for you. However, implementing the basis pursuit and submitting a paper describing it in a professional society journal would become an important and highly cited paper even if someone has done something with basis pursuit already. Even without additional educational credentials, that means more job opportunities and money. The basis pursuit is no more difficult than what you have already done. It's a little more work as there's new software to learn, but I can help you with that by supplying some example cases to play with. Once you've got it set up it would only require modification if the noise environment changed or the assembly design changed. I don't recall if I mentioned it in this thread, but I'm a retired oil industry research scientist/programmer. I worked for three majors, a super major and two large independents. As a contractor I routinely attended industry consortia on behalf of the client at Stanford and other top schools in my field. So I was grilling students and professors on the work they were presenting. It's *very* unusual for a contractor to do that. An all expense paid trip to Palo Alto or the Stanley Hotel in Estes Park (where "The Shining" was filmed) is rather a plum assignment. But I was the last person left after a couple of rounds of "right sizing" who knew enough to do it. Generally I knew about 1/2 the attendees either from working with them at other oil companies or from the annual professional society meetings. I think it worth noting I did not gt my PhD, personality conflicts with my supervisor led to losing my financial support after 4 years. So I'd have had to go to Stanford and spend another 6 years. Losing another 6 years of income living on a grad student stipend was simply too costly. It did prevent my getting jobs where they wanted a PhD to impress the customer, but otherwise had no effect on my earnings or status at work. Most people assumed I had it and were very surprised when i said I didn't. At the PhD level, normal introductory small talk includes inquiring where and under whom someone took their degree. In the case of major consortia such as the Stanford Exploration Project founded by Jon Claerbout and now run by his student Biondo Biondi which has run for 45 years, they will also ask when. That tells them who your classmates were and the work with which you are familiar. I'm not an industrial engineer, so I can't say how much attention sparse L1 pursuits have attracted in that field, but there are two active research consortia in geophysics entirely devoted to the subject. One at U of BC in Vancouver led by Felix Hermann and the other at Alberta led by Mauricio Sacchi. If you're not familiar with industrial research consortia, these are organizations that the big name professors use to raise money to fund their graduate students. Typical fee is $35-55K/yr. For this you get access to the research a couple of years before non-members. In many cases the software is only available to members even after 5-10 years. You also get access to the students and if you pick up the phone and call the professor, he takes your call. He'll also come do a day long short course if requested. I took a quick look at Mallat, but I don't think I could post a long enough scan to be useful. But I have posted a figure from "A Mathematical Introduction to Compressive Sensing" by Foucart and Rauhut. The upper left shows the amplitude of the Fourier coefficients for the time domain trace show below it. From the 64 points in the inverse transform of the upper left, 16 were chosen at random. Only the points chosen are marked and a sin(x)/x interpolator has been applied to the 64 samples generated by doing the inverse transform of the FFT in the upper left. The upper right is the result of attempting to recover the amplitude coefficients from the 16 samples shown in the lower left using an L2 solution of Ax=y. The lower right is the result of solving the same problem using an L1 norm instead. The Nyquist criterion would require all 64 samples to recover the amplitudes using L2. But the L1 case only Shannon applies. A sine wave can be fully described by 3 samples. Shannon showed that we *must* have at least 15 samples to convey the information. In the case of a sparse L1 pursuit the bound is a little higher and 16 samples are needed. But that is 1/4th of the number of samples that the Nyquist criterion requires. So that represents a substantial reduction in the time required to acquire the data. Mallat also treats the problem of removing additive noise, which is why it's too big to post a scan. On the surface this is very different from your problem, but the underlying mathematics are like a magician's bag which changes color every time he turns it inside out. |
| engineheat:
--- Quote from: rhb on January 23, 2019, 10:15:24 pm --- I don't know your age or circumstances, so I can't say whether doing an MS or PhD in industrial engineering makes sense for you. However, implementing the basis pursuit and submitting a paper describing it in a professional society journal would become an important and highly cited paper even if someone has done something with basis pursuit already. Even without additional educational credentials, that means more job opportunities and money. --- End quote --- I got a Masters in Computer Engineering at UIUC and I've decided a Phd is not right for me a few years ago. I am curious and want to be a life long learner. I will explore the basis pursuit later, but for now, I'd like to start with something simple...they do want a solution ASAP because right now the quality checks are manually done. I already ordered a few different microphones. Mind you, not just any mics, but cardioid mics, which hopefully can help filter out ambient noise from the sides... I remember you mentioned piezo sensors. That's also something I want to explore, but I got no experience with them. I think with piezo I can possibly measure the vibrations directly thru contact and this would make ambient noise irrelevant. As I said before, the production line already has a station where a cylinder/actuator will turn on the device by pressing a button, and perform a function check (make sure things are spinning, etc...) The button won't be released until the test is over. I wonder if it's possible to attach a piezo sensor to the head of the actuator to measure the vibration. However, there will be variations to the "press force" due to variations in placements so I wonder if this will ruin my results. Just want to try multiple solutions in parallel. Thanks |
| rhb:
Getting a PhD is expensive and unless you have to have the union card not worth while. I only went back because I had *no* training in seismology at all. I'd been hired into a job because I had a degree in geology and had taken Diff EQ. But I thought DSP was super cool and I wanted better than I could do on my own with a stack of books. When I was hired I was promised several months of training in Tulsa where Amoco had their labs, but I worked for 18 months before I got my only 2 week course. Meanwhile I had to do the stuff we are talking about and more. The only thing that saved me was my boss had an MSEE and I had a ham license. He had come from the labs where he wrote a lot of the DSP codes. So he could translate geophysics speak into radio speak. I also knew optics very well and so all my knowledge of the wave equation was very important. The thing that *is* important about the PhD is acquiring the ability to master a subject for which you have no prior training. I'm immensely proud of having been able to learn sparse L1 pursuits on my own. I won't claim to have mastered it because I've not found anyone to test my knowledge against except at a fairly superficial level. But I do understand it well enough to know that most of the 3000 pages I read are irrelevant to actually applying it. Most of it is just the logical proof that it works and why. There is a similar amount of verbiage that was developed to prove that the Fourier transform worked. As with Heaviside's work, it took the mathematicians a lot of time to develop the logical justification. Get a couple of these to play with (just in case you break one): https://www.stewmac.com/Pickups_and_Electronics/Pickups/Violin_Pickups/Schatten_Soundboard_Transducer.html They are very fragile, so epoxy them to a thin, ceramic disk magnet the same diameter or slightly larger. The neodymium magnets would be too strong. The construction is a thin brass sheet with a pizeo sensor bonded to it and foam to reduce feedback on the top. One possibility would be to remove the foam and cast an epoxy case with an eyelet. An actuator could lower the unit on a string until the magnet grabbed. Leave the string slack while running the test and then pull it away. They are made as light as possible so they don't dampen the guitar top. That doesn't matter in your case. That is specific to the application on an acoustic guitar. There are sensors with broader response, but the prices start going up quickly. These are cheap enough to play with. I glued a small spruce disk to the sensor for reinforcement per factory instructions. They supply butyl tape to attach them and if you try to move one without the spruce reinforcement it will break. The first photo shows the experimental setup. I took a scrap tweeter with a busted cone, stripped off the remains and glued a spruce disk to the end of the cone. Then I swept it with my 33622A from 20 Hz to 20 KHz. The first scope shot shows the input and the output over the full range and the 2nd up to about 8 KHz. The amplitude variation of the input is presumably just mismatch between the 33622A and the inductive load. As the reactance rises, the voltage differential across the terminals should go up. At the time I didn't realize that the 33622A had a high impedance output option so this was a 50 ohm source resistance and the apparent ramp is just the voltage divider effect of the load. One of the applications of sparse L1 pursuits is "blind source separation". Or in simple terms, with a few microphones scattered around a crowded cocktail party, isolate any speaker in the room. It's all a question of setting up the proper A matrix for the problem. I mention that because with a pair of microphones there is a potential to diagnose the exact fault location and thus speed up the rework and collect SPC data. A box made up with sides in the form of drywall - soft foam - acoustic tile with the drywall on the outside and doors at each end that ran in rails vertically would reduce ambient noise a lot and be very amenable to full automation on a fast moving assembly line. Clearly what is needed is a "good enough" solution ASAP. You're already very close to that. The major hurdle of the fundamental mathematics is done. So now it's a question of engineering an implementation which meshes well with the production process. Edison demonstrated how you do that. You try a lot of possible solutions to a problem. Have fun and show me some pictures. |
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