degradation_detection

degradation_detection

DegradationDetectionEvents

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

Bases: Base

Detect degradation patterns in time series signals: trend degradation, variance increases, level shifts, and composite health scores.

Designed for predictive maintenance on manufacturing/industrial IoT data.

detect_trend_degradation

detect_trend_degradation(
    window: str = "1h",
    min_slope: float = 0.0,
    direction: str = "decreasing",
) -> pd.DataFrame

Detect intervals where a rolling linear regression slope exceeds min_slope in the given direction, indicating trend-based degradation.

Parameters:

Name	Type	Description	Default
window	str	Rolling window size (e.g. '1h', '30m').	'1h'
min_slope	float	Minimum absolute slope to consider degradation.	0.0
direction	str	'decreasing' (negative slope) or 'increasing' (positive slope).	'decreasing'

Returns:

Type	Description
DataFrame	DataFrame with columns: start, end, uuid, is_delta, avg_slope,
DataFrame	total_change, duration_seconds.

detect_variance_increase

detect_variance_increase(
    window: str = "1h", threshold_factor: float = 2.0
) -> pd.DataFrame

Compare rolling variance against a baseline (first window) and flag intervals where the ratio exceeds threshold_factor.

Parameters:

Name	Type	Description	Default
window	str	Rolling window size.	'1h'
threshold_factor	float	Minimum variance ratio to flag.	2.0

Returns:

Type	Description
DataFrame	DataFrame with columns: start, end, uuid, is_delta,
DataFrame	baseline_variance, current_variance, ratio.

detect_level_shift

detect_level_shift(
    min_shift: float, hold: str = "5m"
) -> pd.DataFrame

CUSUM-like detection for permanent mean shifts in the signal.

Parameters:

Name	Type	Description	Default
min_shift	float	Minimum absolute shift magnitude to detect.	required
hold	str	Minimum duration the new level must persist.	'5m'

Returns:

Type	Description
DataFrame	DataFrame with columns: systime, uuid, is_delta,
DataFrame	shift_magnitude, prev_mean, new_mean.

health_score

health_score(
    window: str = "1h", baseline_window: str = "24h"
) -> pd.DataFrame

Composite 0-100 health score based on mean drift, variance change, and trend slope, computed over rolling windows.

Parameters:

Name	Type	Description	Default
window	str	Rolling window for current metrics.	'1h'
baseline_window	str	Initial period used to establish baseline behaviour.	'24h'

Returns:

Type	Description
DataFrame	DataFrame with columns: systime, uuid, is_delta,
DataFrame	health_score, mean_drift_pct, variance_ratio, trend_slope.