Manufacturing and industrial assets

Data mapping

Entity

machine or asset

Group

plant or line

Profile

generic; configure time windows with verticals.register

Reference implementation

from datetime import datetime, timedelta
from vertiq import plugins, rules
from vertiq.mapping import Mapping, apply, repair
from vertiq.tasks import predict_event, predict_value

# rows: the source export as a list of dicts, e.g. list(csv.DictReader(open("export.csv")))
mapping = Mapping.parse({"entity": "Equipment No", "time": "Work Order Date", "type": "Order Type", "value": "Cost", "group": "Plant", "attrs": ["Fault Code", "PM Done", "Technician Note"], "date_format": "%Y-%m-%d"})
m, notes = repair(mapping, rows)      # validates column names, value types and date format against the rows
ds, problems = apply(m, rows)         # rows that fail to load are reported, not imputed

@plugins.feature("plants")
def skipped_pm_60(entity, before, cut):   # receives only events recorded before the cutoff
    return {"skipped_pm_60": sum(1 for e in before if e.type == "pm"
                                and e.attrs.get("PM Done") == "no"
                                and e.at >= cut - timedelta(days=60))}

task = predict_event(ds, target="breakdown", window_days=30, pack="plants")
print(task.status, task.report())     # "trained" only if the model outperformed the baseline on held-out entities
if task.status == "trained":
    print(task.score("EQ-602"))  # probability with contributing factors

size = predict_value(ds, target="breakdown", window_days=90, pack="plants")
print(size.status, size.report())

flags = rules.run(ds, [
    rules.repeat(30, key="Fault Code", type="breakdown"),   # the same fault code within 30 days
    rules.exceeds({"SEAL": {"seal kit": 1}}, key="Fault Code"),   # parts consumption above the bill of materials
    rules.too_short("Technician Note", 2),   # technician notes too short to be useful
])
print(flags["summary"])

This code is executed by the test suite (tests/test_guides.py) against generated records with these column names.