Packaging Industry Anomaly DEtection (PIADE) Dataset

PIADE dataset contains data from five industrial packaging machines:

• Machine s_1: from 2020-01-01 14:00:00 to 2021-12-31 13:00:00
• Machine s_2: from 2020-06-17 08:00:00 to 2021-12-31 07:00:00
• Machine s_3: from 2020-10-07 12:00:00 to 2022-01-01 23:00:00
• Machine s_4: from 2020-01-01 01:00:00 to 2022-01-01 23:00:00
• Machine s_5: from 2020-01-20 08:00:00 to 2022-01-01 12:00:00

## Raw Data

Each row represents a production interval, with the following schema:

• interval_start: start of the production interval    
• equipment_ID: equipment identifier    
• alarm: alarm code of the active stop reason, if it occurred     
• type: idle, production, downtime, performance_loss or scheduled_downtime    
• start: start of the production interval    
• end: end of the production interval    
• elapsed: duration of the production interval    
• pi: input packages    
• po: output packages    
• speed: speed (packages per hour)

There are 133 different types of alerts, and 429394 rows.
 

## Sequences (1h) data

For each piece of equipment, we define sequences of length = 1 hour and we aggregate raw interval data as follows:

• 'equipment_ID': machine identifier
• '#changes': changes in machine state
• '%downtime': time spent in 'downtime' state
• '%idle': time spent in 'idle' state
• '%performance_loss': time spent in 'performance loss' state
• '%production': time spent in production
• '%scheduled_downtime': time spent in scheduled downtime
• 'count_sum': sum of all alarm occurrences
• 'A_<XXX>': counter of alarm <XXX> occurrences
• '<state1>/<state2>': number of transitions from <state1> to <state2>