operating_state

operating_state

OperatingStateEvents

OperatingStateEvents(
    dataframe: DataFrame,
    signal_uuid: str,
    *,
    event_uuid: str = "prod:operating_state",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Production: Operating State Inference

Infer discrete operating states from continuous signals (vibration, current, power, temperature) when no explicit boolean state signal is available. Answers the question "what state is the machine in?"

Unlike :class:MachineStateEvents which requires a boolean run/idle signal, this class clusters continuous values to discover states automatically.

Methods: - infer_states: Cluster the signal into N operating states. - state_duration_statistics: How long does each state typically last? - unusual_state_sequence: Did states occur in an unexpected order? - dwell_time_violation: Did the machine stay in a state too long?

infer_states

infer_states(
    n_states: int = 2, min_duration: str = "0s"
) -> pd.DataFrame

Cluster the signal into N operating states.

Uses quantile-based binning to assign each sample to a state, then intervalizes consecutive same-state runs.

Parameters:

Name	Type	Description	Default
n_states	int	Number of states to infer (2 = low/high, 3 = low/mid/high, etc.).	2
min_duration	str	Discard intervals shorter than this.	'0s'

Returns:

Type	Description
DataFrame	DataFrame with columns: start, end, state, state_label,
DataFrame	mean_value, duration_seconds, uuid (event uuid), source_uuid
DataFrame	(signal uuid). Samples without a value are ignored; with fewer
DataFrame	valued samples than n_states the result is empty.

state_duration_statistics

state_duration_statistics(
    n_states: int = 2,
) -> pd.DataFrame

How long does each inferred state typically last?

Parameters:

Name	Type	Description	Default
n_states	int	Number of states to infer.	2

Returns:

Type	Description
DataFrame	DataFrame with columns: state, state_label, count,
DataFrame	mean_duration, std_duration, min_duration, max_duration,
DataFrame	total_duration.

unusual_state_sequence

unusual_state_sequence(
    n_states: int = 2,
    expected_order: list[int] | None = None,
) -> pd.DataFrame

Detect state transitions that violate an expected order.

By default, the expected order is the most common transition sequence observed in the data. Supply expected_order to override (e.g. [0, 1, 2, 1, 0] for a warm-up/run/cool-down cycle).

Parameters:

Name	Type	Description	Default
n_states	int	Number of states to infer.	2
expected_order	list[int] | None	List of state IDs defining the expected cycle. If None, the most frequent bigram transitions are treated as "normal".	None

Returns:

Type	Description
DataFrame	DataFrame with columns: transition_time, from_state,
DataFrame	to_state, from_label, to_label, is_unusual.

dwell_time_violation

dwell_time_violation(
    state: int, max_duration: str, n_states: int = 2
) -> pd.DataFrame

Find intervals where the machine stayed in a state too long.

Parameters:

Name	Type	Description	Default
state	int	State ID to check (0-based).	required
max_duration	str	Maximum allowed duration (e.g. '5min').	required
n_states	int	Number of states to infer.	2

Returns:

Type	Description
DataFrame	DataFrame with columns: start, end, state_label,
DataFrame	duration_seconds, excess_seconds, uuid (event uuid), source_uuid
DataFrame	(signal uuid).