1 00:00:03,376 --> 00:00:06,876 We have learned in this course that data can be used for several objectives. 2 00:00:07,336 --> 00:00:11,366 The first is simply just to learn about your process, and understand what is going on. 3 00:00:11,866 --> 00:00:16,026 For example, in the section on t-tests we learned how to verify whether changes 4 00:00:16,026 --> 00:00:18,426 to our process are actually significant or not. 5 00:00:18,696 --> 00:00:21,996 We can also learn by observing trends in the data visualizations, 6 00:00:22,296 --> 00:00:24,236 to troubleshoot problems, and so forth. 7 00:00:24,776 --> 00:00:27,696 In this video we are going to look at process monitoring, 8 00:00:28,126 --> 00:00:30,996 where we build on that topic and take it step further. 9 00:00:31,536 --> 00:00:35,216 Other interesting things that we can do with our data are to build predictive models, 10 00:00:35,216 --> 00:00:37,586 such as a least squares model to make a prediction 11 00:00:37,586 --> 00:00:39,966 of a property that's really hard to measure. 12 00:00:40,676 --> 00:00:44,746 We've also just recently seen how we can optimize our processes using response 13 00:00:44,746 --> 00:00:45,556 surface methods. 14 00:00:46,146 --> 00:00:50,476 Climbing that mountain and maximizing some value on our process, such as the profit. 15 00:00:51,066 --> 00:00:56,186 The last section here considers the fifth major objective, that of process monitoring. 16 00:00:56,716 --> 00:01:01,206 Process monitoring is a way that we can track how our system behaves in real time, 17 00:01:01,516 --> 00:01:03,766 to ensure that it remains on target. 18 00:01:04,346 --> 00:01:08,776 Any patterns that we observe in these monitoring plots are then used for troubleshooting. 19 00:01:09,466 --> 00:01:12,636 It nicely ties the second and the first objectives together. 20 00:01:13,116 --> 00:01:15,926 Let's start with a few monitoring examples that you've seen before. 21 00:01:16,526 --> 00:01:18,196 The first is that of a hospital. 22 00:01:18,386 --> 00:01:22,856 We've all seen this on TV, where a patient is being monitored for various vital signs 23 00:01:23,116 --> 00:01:28,096 such as heartbeat, blood pressure, oxygen level, blood glucose level, body temperature. 24 00:01:28,206 --> 00:01:29,936 These are all parameters that now go 25 00:01:29,936 --> 00:01:33,126 into hospital databases to track patients in real time. 26 00:01:33,636 --> 00:01:37,126 Perhaps you have seen these monitoring charts in a control room in a chemical plant, 27 00:01:37,416 --> 00:01:41,386 or perhaps you've observed stock market charts and people who do intra-day trading. 28 00:01:41,936 --> 00:01:46,156 In an engineering context, we can monitor our processes for their vital signs 29 00:01:46,446 --> 00:01:50,536 to ensure they're behaving on-target and away from unsafe operation. 30 00:01:51,076 --> 00:01:53,026 When problems are observed in these charts, 31 00:01:53,136 --> 00:01:55,406 engineers and operators quickly react quickly to them. 32 00:01:55,906 --> 00:02:00,066 Notice that process monitoring is a reactive step, it is not proactive. 33 00:02:00,766 --> 00:02:04,536 Optimization, which we saw earlier in this course, is a proactive activity. 34 00:02:05,006 --> 00:02:09,416 There we proactively moved the process to a better location or operating point. 35 00:02:09,986 --> 00:02:14,476 More than any other aspect that we've learned about in this course, this area of monitoring 36 00:02:14,476 --> 00:02:17,496 and data acquisition is quickly growing in importance. 37 00:02:17,846 --> 00:02:21,946 You may have heard of the terms big data, lean manufacturing, "six sigma". 38 00:02:22,656 --> 00:02:25,196 All of these come out of this topic of process monitoring. 39 00:02:25,696 --> 00:02:27,636 If you want to learn more about this area, 40 00:02:27,816 --> 00:02:30,516 I strongly suggest reading these good books shown here 41 00:02:30,516 --> 00:02:32,706 on the screen for an engineering context. 42 00:02:33,126 --> 00:02:37,246 However, I can guarantee this topic will be quite different, even five years from now. 43 00:02:37,726 --> 00:02:41,036 Let's start off though and look at the workhorse of process monitoring. 44 00:02:41,276 --> 00:02:45,696 This is a chart that has been around for almost a century, it's not going to go away. 45 00:02:46,186 --> 00:02:50,466 The first feature that you notice is the fact that it is a time series or sequence plot, 46 00:02:50,936 --> 00:02:53,266 where new points are added on the right-hand side, 47 00:02:53,526 --> 00:02:56,816 and previous points get removed, or disappear, on the left. 48 00:02:57,286 --> 00:03:00,766 It is displayed in real time, or as close to real time as possible. 49 00:03:01,236 --> 00:03:05,066 The units on the vertical axis are the units of the variable being measured. 50 00:03:05,596 --> 00:03:10,226 There might be several horizontal lines also drawn, one of which is the target line. 51 00:03:10,616 --> 00:03:13,626 There might also be upper control limits and lower control limits. 52 00:03:13,816 --> 00:03:16,216 We'll see how these are derived in the coming videos. 53 00:03:16,826 --> 00:03:21,326 I'm going to start with a demonstration though that will quickly illustrate how these are used. 54 00:03:21,986 --> 00:03:25,056 Here is an example of an actual system I had a chance to work on. 55 00:03:25,516 --> 00:03:29,896 The company was monitoring the appearance of bubbles on the top of a flotation froth. 56 00:03:30,876 --> 00:03:34,636 Flotation is a process whereby minerals attach themselves to a bubble; 57 00:03:34,806 --> 00:03:37,206 float to the top of the tank and are removed. 58 00:03:37,686 --> 00:03:41,616 Air is bubbled into the system and mixed in to assist this process. 59 00:03:41,966 --> 00:03:45,616 The company places a digital video camera over the top of the tank 60 00:03:45,686 --> 00:03:47,556 and observes the appearance of the bubble. 61 00:03:47,946 --> 00:03:50,976 Some examples of these images are now shown here on the screen. 62 00:03:51,546 --> 00:03:55,126 One of the parameters the operators are interested in, is the bubble size. 63 00:03:55,386 --> 00:03:59,106 They're also interested in the bubble's colour, and other textural features. 64 00:03:59,526 --> 00:04:03,926 I'm showing an accelerated version of the two monitoring charts developed for this process. 65 00:04:04,206 --> 00:04:08,136 The first is the bubble diameter, measured in millimetres, and the lower 66 00:04:08,136 --> 00:04:11,076 and upper control limits are shown, as well as the target value. 67 00:04:11,436 --> 00:04:17,476 The second chart monitors the grey-scale colour, which is a scale from 0 to 255, 68 00:04:17,786 --> 00:04:19,616 and there are also limits for this value. 69 00:04:20,066 --> 00:04:22,876 Now at some point in time the following might take place. 70 00:04:23,116 --> 00:04:25,246 We see the bubble diameter has shifted down. 71 00:04:25,656 --> 00:04:28,766 The grey-scale colour has also changed, and shifted up. 72 00:04:29,556 --> 00:04:33,366 The operator notices this signature, of a very particular problem 73 00:04:33,366 --> 00:04:35,406 that occurs periodically in the process. 74 00:04:35,826 --> 00:04:39,386 When they are alerted to this, through an automated alarm process, 75 00:04:39,606 --> 00:04:42,466 they know exactly what to do to counteract the problem. 76 00:04:42,876 --> 00:04:47,216 Notice however that the monitoring chart will never conclusively tell you exactly what is 77 00:04:47,216 --> 00:04:48,186 wrong with the process. 78 00:04:48,556 --> 00:04:51,316 It will simply alert you to the fact that something is wrong. 79 00:04:51,566 --> 00:04:53,596 You will have to use your judgement and knowledge 80 00:04:53,596 --> 00:04:55,906 of the physical system to the troubleshooting. 81 00:04:56,446 --> 00:05:00,206 This is no different to a nurse or a doctor in a medical facility who will have 82 00:05:00,206 --> 00:05:04,876 to observe the signals being shown on the medical devices to determine the problem is, 83 00:05:05,336 --> 00:05:07,946 and then make the subsequent diagnosis to fix it. 84 00:05:08,336 --> 00:05:13,246 Notice however that a variety of different problems can have the same signature 85 00:05:13,246 --> 00:05:14,046 on the chart. 86 00:05:14,556 --> 00:05:19,216 There is never a one-to-one relationship between a problem, and its signature on the plot. 87 00:05:19,556 --> 00:05:22,276 This is why there is always human intervention required 88 00:05:22,276 --> 00:05:24,586 to diagnose, and then to fix the problem. 89 00:05:25,056 --> 00:05:29,646 This chart that we've just used is called the Shewhart chart, and named after Walter Shewhart 90 00:05:29,646 --> 00:05:33,076 from the Bell Telephone Company, who developed in the 1920s 91 00:05:33,076 --> 00:05:35,706 to monitor the production of parts at the phone company. 92 00:05:36,156 --> 00:05:40,946 It is a chart for monitoring the location of a variable; where it lies on that vertical axis. 93 00:05:41,496 --> 00:05:44,716 Shewhart charts often have a lower and an upper control limit, 94 00:05:45,056 --> 00:05:47,006 as well as a target line drawn on them. 95 00:05:47,336 --> 00:05:51,766 A process is considered to be "in control" if it lies within those limits. 96 00:05:52,236 --> 00:05:54,606 The opposite is a process being "out of control", 97 00:05:55,006 --> 00:05:59,146 when it lies beyond the upper control limit or below the lower control limit. 98 00:05:59,466 --> 00:06:03,036 We say that a process is in control when there is variation, 99 00:06:03,446 --> 00:06:06,766 but we call that common cause variability, within the limits. 100 00:06:07,186 --> 00:06:13,446 Regular operation is stable, the product being produced has variability, but it is still sold 101 00:06:13,446 --> 00:06:15,866 to the customer as good quality product. 102 00:06:16,476 --> 00:06:21,746 When we are out of control, we say that a special cause, or special causes, have occurred. 103 00:06:22,276 --> 00:06:27,536 Some destabilizing event has happened, we are out of control, we are "off-target". 104 00:06:27,866 --> 00:06:30,816 This is product that we typically will not sell to our customer. 105 00:06:31,166 --> 00:06:35,576 We may have to modify it, or sell it at a lower price, or even destroy it. 106 00:06:36,146 --> 00:06:39,866 Now one of the toughest problems that engineers often face is to figure out which 107 00:06:39,866 --> 00:06:42,896 of the many variables available to us, should be monitored. 108 00:06:43,326 --> 00:06:46,906 Companies have hundreds, if not thousands, of variables available to them. 109 00:06:46,906 --> 00:06:49,726 Especially on newer plants with multiple sensors. 110 00:06:50,076 --> 00:06:54,386 Consider the following situations before we continue on: What would you monitor 111 00:06:54,386 --> 00:06:56,856 if you were running a waste-water treatment process? 112 00:06:57,426 --> 00:07:00,426 Which variables would you monitor in an oil and gas facility? 113 00:07:01,066 --> 00:07:05,756 What might be of interest to track in a food processing unit, or a mineral processing plant? 114 00:07:06,126 --> 00:07:07,906 Or what if you were producing plastics? 115 00:07:08,366 --> 00:07:12,976 Which variables monitor the key quality properties in each of those situations? 116 00:07:13,356 --> 00:07:15,136 What about a pharmaceutical facility, 117 00:07:15,136 --> 00:07:18,736 how do we know we are producing good quality product in that location? 118 00:07:19,266 --> 00:07:23,426 Once you have identified which variable you would like to monitor, we can then go 119 00:07:23,426 --> 00:07:25,426 and start constructing a monitoring chart. 120 00:07:25,836 --> 00:07:28,616 Figuring out what the upper control limit is that we should use, 121 00:07:28,616 --> 00:07:32,136 what the lower control limit should be, and what should the target value be? 122 00:07:32,546 --> 00:07:36,216 In the process monitoring literature that step of building the chart, 123 00:07:36,416 --> 00:07:41,786 of figuring out those limits and testing the chart on prior operating data is called phase 1. 124 00:07:42,146 --> 00:07:44,656 This is where you will spend most of your time as an engineer. 125 00:07:45,226 --> 00:07:50,166 Phase 2 is the phase where we go and use this chart on new data that we've never seen before. 126 00:07:50,626 --> 00:07:54,726 This is where the operators and the final end-users of your chart will spend their time. 127 00:07:55,216 --> 00:07:59,696 In the next video we will look at the phase 1 construction of a Shewhart chart.