Power BI is turning 10! Tune in for a special live episode on July 24 with behind-the-scenes stories, product evolution highlights, and a sneak peek at what’s in store for the future.
Save the dateEnhance your career with this limited time 50% discount on Fabric and Power BI exams. Ends August 31st. Request your voucher.
Hi everyone,
I am having trouble to create a solution that allows me to identify the top three contributing factors to a machine stop. The only events I would like to consider are events that happened 6 minutes before the machine stopped. This is my scenario:
How can I identify the top events that caused machine 2 to stop (only considering events that happened 6 minutes before the stop in machine 2 and 3)?
Please see the table below for an example with mock data.
Thank you.
Date Time Machine Event
17/03/2021 | 12:02:30 AM | Machine 2 | STOP |
17/03/2021 | 12:14:25 AM | Machine 1 | Arms not closed |
17/03/2021 | 12:14:26 AM | Machine 1 | Arms not closed |
17/03/2021 | 12:14:28 AM | Machine 1 | Arms not closed |
17/03/2021 | 12:14:57 AM | Machine 1 | Arms not closed |
17/03/2021 | 12:38:05 AM | Machine 1 | Arms not closed |
17/03/2021 | 12:42:06 AM | Machine 1 | Arms not closed |
17/03/2021 | 12:43:06 AM | Machine 1 | Arms not closed |
17/03/2021 | 12:43:24 AM | Machine 1 | Arms not closed |
17/03/2021 | 1:21:57 AM | Machine 2 | STOP |
17/03/2021 | 1:56:50 AM | Machine 2 | STOP |
17/03/2021 | 2:38:14 AM | Machine 2 | STOP |
17/03/2021 | 2:52:39 AM | Machine 1 | Arm fault |
17/03/2021 | 2:54:30 AM | Machine 2 | STOP |
17/03/2021 | 3:00:14 AM | Machine 1 | Arm fault |
17/03/2021 | 3:17:03 AM | Machine 2 | STOP |
17/03/2021 | 3:24:29 AM | Machine 1 | Arm fault |
17/03/2021 | 3:28:22 AM | Machine 2 | STOP |
17/03/2021 | 3:38:20 AM | Machine 2 | STOP |
17/03/2021 | 4:18:54 AM | Machine 2 | STOP |
17/03/2021 | 4:39:01 AM | Machine 1 | Arm fault |
17/03/2021 | 4:40:45 AM | Machine 2 | STOP |
17/03/2021 | 5:05:20 AM | Machine 1 | Motor fault |
17/03/2021 | 9:56:11 PM | Machine 1 | Top Limit reached |
17/03/2021 | 00:03:44 | Machine 3 | Error Code C19 |
17/03/2021 | 00:03:46 | Machine 3 | Error Code C2 |
17/03/2021 | 00:03:52 | Machine 3 | Error Code C3 |
17/03/2021 | 00:05:25 | Machine 3 | Error Code C19 |
17/03/2021 | 00:05:27 | Machine 3 | Error Code C2 |
17/03/2021 | 00:05:33 | Machine 3 | Error Code C3 |
17/03/2021 | 00:07:53 | Machine 3 | Error Code C19 |
17/03/2021 | 00:07:55 | Machine 3 | Error Code C2 |
17/03/2021 | 00:08:01 | Machine 3 | Error Code C3 |
17/03/2021 | 00:08:58 | Machine 3 | Error Code C19 |
17/03/2021 | 00:09:00 | Machine 3 | Error Code C2 |
17/03/2021 | 00:09:06 | Machine 3 | Error Code C3 |
17/03/2021 | 00:10:22 | Machine 3 | Error Code C19 |
17/03/2021 | 00:10:24 | Machine 3 | Error Code C2 |
17/03/2021 | 00:10:30 | Machine 3 | Error Code C3 |
17/03/2021 | 00:11:39 | Machine 3 | Pick-up error |
17/03/2021 | 00:12:22 | Machine 3 | Clear carton |
17/03/2021 | 01:04:21 | Machine 3 | Backup chain not clear |
17/03/2021 | 01:53:10 | Machine 3 | Belt good not clear |
17/03/2021 | 01:54:36 | Machine 3 | Error Code C19 |
17/03/2021 | 01:54:38 | Machine 2 | STOP |
17/03/2021 | 01:54:43 | Machine 3 | Error Code C3 |
17/03/2021 | 02:00:47 | Machine 3 | Backup chain not clear |
17/03/2021 | 02:03:20 | Machine 3 | Error Code C19 |
17/03/2021 | 02:03:22 | Machine 3 | Error Code C2 |
17/03/2021 | 02:03:25 | Machine 3 | Error Code C3 |
17/03/2021 | 02:05:33 | Machine 2 | STOP |
Hi @amitchandak,
Thanks so much for your quick response.
Unfortunately, your code does not present the desired results. Here is what I entered:
@Julius410 , Use the second measure with event in the table visual
M1= calculate(countrows(table), filter(Table, Table[Date Time] >= now() -time(6,0,0) && Table[Machine] = "Machine2"))
Top 2 =calculate([M1], TOPN(2,allselected(Table[Event]),[M1],DESC), values(Table[Event]))
Check out the July 2025 Power BI update to learn about new features.
User | Count |
---|---|
25 | |
10 | |
7 | |
6 | |
6 |
User | Count |
---|---|
30 | |
11 | |
11 | |
10 | |
6 |