failure_prediction

failure_prediction ¤

FailurePredictionEvents ¤

FailurePredictionEvents(
    dataframe: DataFrame,
    signal_uuid: str,
    *,
    event_uuid: str = "maint:failure_pred",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Predict remaining useful life and detect escalating failure patterns in time series signals from manufacturing/industrial IoT systems.

remaining_useful_life ¤

remaining_useful_life(
    degradation_rate: float, failure_threshold: float
) -> pd.DataFrame

Estimate remaining useful life (RUL) at each point via linear extrapolation from the recent trend to a failure threshold.

Parameters:

Name	Type	Description	Default
`degradation_rate`	`float`	Expected rate of change per second (used as fallback if local slope cannot be computed).	required
`failure_threshold`	`float`	The signal level that represents failure. The sign of `degradation_rate` gives the failure direction (positive: failure when the value rises to the threshold).	required

Returns:

Type	Description
`DataFrame`	DataFrame with columns: systime, uuid, source_uuid, is_delta,
`DataFrame`	current_value, rul_seconds, rul_hours, confidence. `rul_*` is 0 once the
`DataFrame`	threshold is reached or passed, and None when no failure is
`DataFrame`	projected (flat trend, or a trend moving away from the threshold).

detect_exceedance_pattern ¤

detect_exceedance_pattern(
    warning_threshold: float,
    critical_threshold: float,
    window: str = "1h",
) -> pd.DataFrame

Track frequency of threshold exceedances in rolling windows and flag escalating patterns (increasing exceedance counts).

Parameters:

Name	Type	Description	Default
`warning_threshold`	`float`	Warning level (absolute value comparison).	required
`critical_threshold`	`float`	Critical level (absolute value comparison).	required
`window`	`str`	Rolling window size.	`'1h'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, uuid,
`DataFrame`	source_uuid, is_delta, warning_count, critical_count,
`DataFrame`	escalation_detected. `end` is `start` plus `window`.

time_to_threshold ¤

time_to_threshold(
    threshold: float, direction: str = "increasing"
) -> pd.DataFrame

Estimate time to reach a threshold based on recent rate of change.

Parameters:

Name	Type	Description	Default
`threshold`	`float`	Target threshold value.	required
`direction`	`str`	'increasing' or 'decreasing'.	`'increasing'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: systime, uuid, source_uuid, is_delta,
`DataFrame`	current_value, rate_of_change, estimated_time_seconds.