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On March 6, 2023 at 2:09:10 PM UTC, Saleh Seyedzadeh:
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f | 1 | { | f | 1 | { |
2 | "author": "Stephan Matzka", | 2 | "author": "Stephan Matzka", | ||
3 | "author_email": "", | 3 | "author_email": "", | ||
4 | "creator_user_id": "fe29d92f-4473-4767-8e12-4429678cd91e", | 4 | "creator_user_id": "fe29d92f-4473-4767-8e12-4429678cd91e", | ||
5 | "extras": [], | 5 | "extras": [], | ||
6 | "groups": [], | 6 | "groups": [], | ||
7 | "id": "730c1340-96f7-4ea8-a540-33e4b8b16c86", | 7 | "id": "730c1340-96f7-4ea8-a540-33e4b8b16c86", | ||
8 | "isopen": true, | 8 | "isopen": true, | ||
9 | "license_id": "gfdl", | 9 | "license_id": "gfdl", | ||
10 | "license_title": "GNU Free Documentation License", | 10 | "license_title": "GNU Free Documentation License", | ||
11 | "license_url": "http://www.opendefinition.org/licenses/gfdl", | 11 | "license_url": "http://www.opendefinition.org/licenses/gfdl", | ||
12 | "maintainer": "", | 12 | "maintainer": "", | ||
13 | "maintainer_email": "", | 13 | "maintainer_email": "", | ||
14 | "metadata_created": "2023-03-06T14:07:57.719002", | 14 | "metadata_created": "2023-03-06T14:07:57.719002", | ||
n | 15 | "metadata_modified": "2023-03-06T14:09:10.141443", | n | 15 | "metadata_modified": "2023-03-06T14:09:10.880419", |
16 | "name": "predictive-maintenance", | 16 | "name": "predictive-maintenance", | ||
17 | "notes": "This synthetic dataset is modeled after an existing | 17 | "notes": "This synthetic dataset is modeled after an existing | ||
18 | milling machine and consists of 10 000 data points from a stored as | 18 | milling machine and consists of 10 000 data points from a stored as | ||
19 | rows with 14 features in columns\r\n\r\n", | 19 | rows with 14 features in columns\r\n\r\n", | ||
20 | "num_resources": 1, | 20 | "num_resources": 1, | ||
21 | "num_tags": 0, | 21 | "num_tags": 0, | ||
22 | "organization": { | 22 | "organization": { | ||
23 | "approval_status": "approved", | 23 | "approval_status": "approved", | ||
24 | "created": "2023-03-06T14:06:37.804321", | 24 | "created": "2023-03-06T14:06:37.804321", | ||
25 | "description": "Scotland's innovation centre for data science and | 25 | "description": "Scotland's innovation centre for data science and | ||
26 | AI. Our purpose is to change lives by transforming the way we use | 26 | AI. Our purpose is to change lives by transforming the way we use | ||
27 | data", | 27 | data", | ||
28 | "id": "9d5ac28f-59cb-41f3-8378-c2e2f43617b5", | 28 | "id": "9d5ac28f-59cb-41f3-8378-c2e2f43617b5", | ||
29 | "image_url": "2023-03-06-140637.788856TDL-logo.jpg", | 29 | "image_url": "2023-03-06-140637.788856TDL-logo.jpg", | ||
30 | "is_organization": true, | 30 | "is_organization": true, | ||
31 | "name": "the-data-lab", | 31 | "name": "the-data-lab", | ||
32 | "state": "active", | 32 | "state": "active", | ||
33 | "title": "The Data Lab", | 33 | "title": "The Data Lab", | ||
34 | "type": "organization" | 34 | "type": "organization" | ||
35 | }, | 35 | }, | ||
36 | "owner_org": "9d5ac28f-59cb-41f3-8378-c2e2f43617b5", | 36 | "owner_org": "9d5ac28f-59cb-41f3-8378-c2e2f43617b5", | ||
37 | "private": false, | 37 | "private": false, | ||
38 | "relationships_as_object": [], | 38 | "relationships_as_object": [], | ||
39 | "relationships_as_subject": [], | 39 | "relationships_as_subject": [], | ||
40 | "resources": [ | 40 | "resources": [ | ||
41 | { | 41 | { | ||
42 | "cache_last_updated": null, | 42 | "cache_last_updated": null, | ||
43 | "cache_url": null, | 43 | "cache_url": null, | ||
44 | "created": "2023-03-06T14:09:10.156826", | 44 | "created": "2023-03-06T14:09:10.156826", | ||
45 | "datastore_active": false, | 45 | "datastore_active": false, | ||
46 | "description": "UID: unique identifier ranging from 1 to | 46 | "description": "UID: unique identifier ranging from 1 to | ||
47 | 10000\r\nproduct ID: consisting of a letter L, M, or H for low (50% of | 47 | 10000\r\nproduct ID: consisting of a letter L, M, or H for low (50% of | ||
48 | all products), medium (30%) and high (20%) as product quality variants | 48 | all products), medium (30%) and high (20%) as product quality variants | ||
49 | and a variant-specific serial number\r\ntype: just the product type L, | 49 | and a variant-specific serial number\r\ntype: just the product type L, | ||
50 | M or H from column 2\r\nair temperature [K]: generated using a random | 50 | M or H from column 2\r\nair temperature [K]: generated using a random | ||
51 | walk process later normalized to a standard deviation of 2 K around | 51 | walk process later normalized to a standard deviation of 2 K around | ||
52 | 300 K\r\nprocess temperature [K]: generated using a random walk | 52 | 300 K\r\nprocess temperature [K]: generated using a random walk | ||
53 | process normalized to a standard deviation of 1 K, added to the air | 53 | process normalized to a standard deviation of 1 K, added to the air | ||
54 | temperature plus 10 K.\r\nrotational speed [rpm]: calculated from a | 54 | temperature plus 10 K.\r\nrotational speed [rpm]: calculated from a | ||
55 | power of 2860 W, overlaid with a normally distributed noise\r\ntorque | 55 | power of 2860 W, overlaid with a normally distributed noise\r\ntorque | ||
56 | [Nm]: torque values are normally distributed around 40 Nm with a SD = | 56 | [Nm]: torque values are normally distributed around 40 Nm with a SD = | ||
57 | 10 Nm and no negative values.\r\ntool wear [min]: The quality variants | 57 | 10 Nm and no negative values.\r\ntool wear [min]: The quality variants | ||
58 | H/M/L add 5/3/2 minutes of tool wear to the used tool in the | 58 | H/M/L add 5/3/2 minutes of tool wear to the used tool in the | ||
59 | process.\r\na 'machine failure' label that indicates, whether the | 59 | process.\r\na 'machine failure' label that indicates, whether the | ||
60 | machine has failed in this particular datapoint for any of the | 60 | machine has failed in this particular datapoint for any of the | ||
61 | following failure modes are true.\r\nThe machine failure consists of | 61 | following failure modes are true.\r\nThe machine failure consists of | ||
62 | five independent failure modes\r\n\r\ntool wear failure (TWF): the | 62 | five independent failure modes\r\n\r\ntool wear failure (TWF): the | ||
63 | tool will be replaced of fail at a randomly selected tool wear time | 63 | tool will be replaced of fail at a randomly selected tool wear time | ||
64 | between 200 - 240 mins (120 times in our dataset). At this point in | 64 | between 200 - 240 mins (120 times in our dataset). At this point in | ||
65 | time, the tool is replaced 69 times, and fails 51 times (randomly | 65 | time, the tool is replaced 69 times, and fails 51 times (randomly | ||
66 | assigned).\r\nheat dissipation failure (HDF): heat dissipation causes | 66 | assigned).\r\nheat dissipation failure (HDF): heat dissipation causes | ||
67 | a process failure, if the difference between air- and process | 67 | a process failure, if the difference between air- and process | ||
68 | temperature is below 8.6 K and the tools rotational speed is below | 68 | temperature is below 8.6 K and the tools rotational speed is below | ||
69 | 1380 rpm. This is the case for 115 data points.\r\npower failure | 69 | 1380 rpm. This is the case for 115 data points.\r\npower failure | ||
70 | (PWF): the product of torque and rotational speed (in rad/s) equals | 70 | (PWF): the product of torque and rotational speed (in rad/s) equals | ||
71 | the power required for the process. If this power is below 3500 W or | 71 | the power required for the process. If this power is below 3500 W or | ||
72 | above 9000 W, the process fails, which is the case 95 times in our | 72 | above 9000 W, the process fails, which is the case 95 times in our | ||
73 | dataset.\r\noverstrain failure (OSF): if the product of tool wear and | 73 | dataset.\r\noverstrain failure (OSF): if the product of tool wear and | ||
74 | torque exceeds 11,000 minNm for the L product variant (12,000 M, | 74 | torque exceeds 11,000 minNm for the L product variant (12,000 M, | ||
75 | 13,000 H), the process fails due to overstrain. This is true for 98 | 75 | 13,000 H), the process fails due to overstrain. This is true for 98 | ||
76 | datapoints.\r\nrandom failures (RNF): each process has a chance of 0,1 | 76 | datapoints.\r\nrandom failures (RNF): each process has a chance of 0,1 | ||
77 | % to fail regardless of its process parameters. This is the case for | 77 | % to fail regardless of its process parameters. This is the case for | ||
78 | only 5 datapoints, less than could be expected for 10,000 datapoints | 78 | only 5 datapoints, less than could be expected for 10,000 datapoints | ||
79 | in our dataset.\r\nIf at least one of the above failure modes is true, | 79 | in our dataset.\r\nIf at least one of the above failure modes is true, | ||
80 | the process fails and the 'machine failure' label is set to 1. It is | 80 | the process fails and the 'machine failure' label is set to 1. It is | ||
81 | therefore not transparent to the machine learning method, which of the | 81 | therefore not transparent to the machine learning method, which of the | ||
82 | failure modes has caused the process to fail.", | 82 | failure modes has caused the process to fail.", | ||
83 | "format": "CSV", | 83 | "format": "CSV", | ||
84 | "hash": "", | 84 | "hash": "", | ||
85 | "id": "5e5d639f-143c-46af-a881-37c30cc90147", | 85 | "id": "5e5d639f-143c-46af-a881-37c30cc90147", | ||
86 | "last_modified": null, | 86 | "last_modified": null, | ||
t | 87 | "metadata_modified": "2023-03-06T14:09:10.147479", | t | 87 | "metadata_modified": "2023-03-06T14:09:10.886814", |
88 | "mimetype": null, | 88 | "mimetype": null, | ||
89 | "mimetype_inner": null, | 89 | "mimetype_inner": null, | ||
90 | "name": " AI4I 2020 Predictive Maintenance Dataset", | 90 | "name": " AI4I 2020 Predictive Maintenance Dataset", | ||
91 | "package_id": "730c1340-96f7-4ea8-a540-33e4b8b16c86", | 91 | "package_id": "730c1340-96f7-4ea8-a540-33e4b8b16c86", | ||
92 | "position": 0, | 92 | "position": 0, | ||
93 | "resource_type": null, | 93 | "resource_type": null, | ||
94 | "size": null, | 94 | "size": null, | ||
95 | "state": "active", | 95 | "state": "active", | ||
96 | "url": "", | 96 | "url": "", | ||
97 | "url_type": null | 97 | "url_type": null | ||
98 | } | 98 | } | ||
99 | ], | 99 | ], | ||
100 | "state": "draft", | 100 | "state": "draft", | ||
101 | "tags": [], | 101 | "tags": [], | ||
102 | "title": "Predictive Maintenance", | 102 | "title": "Predictive Maintenance", | ||
103 | "type": "dataset", | 103 | "type": "dataset", | ||
104 | "url": | 104 | "url": | ||
105 | ive.ics.uci.edu/ml/datasets/AI4I+2020+Predictive+Maintenance+Dataset", | 105 | ive.ics.uci.edu/ml/datasets/AI4I+2020+Predictive+Maintenance+Dataset", | ||
106 | "version": "" | 106 | "version": "" | ||
107 | } | 107 | } |