wtphm.clustering.event_probs

This module is for working with events data from wind turbines. It looks at all eventes generated and sees if there are some events which trigger others. Event A triggers Event B if: t_s_A <= t_s_B and t_e_A >= t_s_B

So we can find the probability that any given A event (known as a parent event) has triggered any B events, and the probability that any given B event (known as a child event) has been triggered by any A events.

wtphm.clustering.event_probs.get_trig_summary(events, codes, tsa_op1='ge', tsa_op2='le', t_hi=0.9, t_lo=0.1)

Gets probabilities that pairs of events will trigger one another, and the derived relationship between these pairs

This function takes a list of event codes. It finds all combinations of pairs of codes from this and splits them into “A” and “B” codes. It then counts the number of events with code A which have triggered one or more events with code B and vice-versa. It then computes a probability that if an A event occurs, it will trigger a B event, and vice-versa. From there, it deduces the relationship between pairs of events, as derived from [1].

Event A is triggered by Event B if:

T_s_A >= T_s_B & T_s_A <= T_e_B

where T_s_A, T_s_B and T_e_B are the start time of events A and B, and the end time of event B, respectively.

Parameters:

  • events (pandas.DataFrame) – The events data from a wind turbine. Must be free of NA values.

  • codes (list-like) – The event codes to look at

  • tsa_op1 (String, default ‘ge’) – Operator to use for T_s_A >= T_s_B or T_s_A > T_s_B. Can be one of:

    ‘ge’: <= ‘gt’: <

  • tsa_op2 (String (default ‘le’)) – Operator to use for T_s_A <= T_e_B or T_s_A < T_e_B. Can be one of: ‘le’: >= ‘lt’: >

  • t_hi (float (default 0.9)) – Threshold of % of A events which trigger B events at or above which relationship 3 is True (or % B triggering A for relationship 4, or % of both for relationship 1). See ‘relationship’ in the returned trig_summary dataframe below.

  • t_low (float (default 0.1)) – Threshold of % of A events which trigger B events (or vice-versa) at or below which relationship 2 is True. See ‘relationship’ in the returned trig_summary dataframe below.
Returns:

trig_summary (Pandas.DataFrame) – A matrix consisting of the following:

  • A_code: the event code of the “A” events

  • A_desc: description of the “A” events

  • B_code: the event code of the “B” events

  • B_desc: description of the “B” events

  • A_count: number of “A” events in the data

  • A_trig_B_count: number of “A” events which trigger one or more “B” events

  • A_trig_B_prob: ratio of “A” events which have triggered one or more “B” events, to the total number of “A” events

  • B_count: Number of “B” events in the data

  • B_trig_A_count: number of “B” events which trigger one or more “A” events

  • B_trig_A_prob: ratio of “B” events which have triggered one or more “A” events, to the total number of “B” events

  • relationship: Number 1-5 indicating the relationship events A have to events B:

    1. High proportion of As trigger Bs & high proportion of Bs trigger As. Alarm A & B usually appear together; A ~= B
    2. Low proportion of As trigger Bs & low proportion of Bs trigger As. A & B never or rarely appear together; A n B ~= 0
    3. High proportion of As trigger Bs & less than high proportion of Bs trigger As. B will usually be triggered whenever alarm A appears - B is a more general alarm; A e B
    4. High proportion of Bs trigger As & less than high proportion of As trigger Bs. A will usually be triggered whenever alarm B appears - A is a more general alarm; B e A
    5. None of the above. The two alarms are randomly or somewhat related; A n B != 0

References

[1] Qiu et al. (2012). Wind turbine SCADA alarm analysis for improving reliability. Wind Energy, 15(8), 951–966. http://doi.org/10.1002/we.513

wtphm.clustering.event_probs.short_summary(trig_summary, codes, t=0.7)

Returns an even more summarised version of trig_summary, showing important relationships

Parameters:
  • trig_summary (Pandas.DataFrame) – Must be the trig_summary obtained from get_trig_summary()
  • codes (int, list) – A single, or list of, event code(s) of interest, i.e. the events that trigger other events
  • t (float) – The threshold for a ‘significant’ relationship. E.g., if t=0.7, only events that trigger other events with a probability >= 0.7 will be displayed.
Returns:

  • df (Pandas.DataFrame)
  • A dataframe consisting of the following –
    • parent_code: the triggering events code
    • child_code: the triggered events code
    • trig_prob: the probability that parent_code events will trigger child_code events
    • trig_count: the count of parent_code events which have triggered child_code events
wtphm.clustering.event_probs.get_trig_summary_verbose(events, codes, tsa_op1='ge', tsa_op2='le')

Gets probabilities that certain events will trigger others, and that certain events will be triggered by others. Can be calculated via a duration-based method, or straightforward count.

This takes a list of event codes. It creates two separate sets of “parent” and “child” events, with all the parent events having the same event code and all the child events having another event code (though it does not necessarily have to be different). It then iterates through every parent event instance to see if it has triggered one or more child events. It counts the number of parent events which have triggered one or more child events for each event code. It also gives a probability that any new parent event will trigger a child event by finding the ratio of parent events which have triggered a child event to those which haven’t.

Event A is triggered by Event B if:

T_s_A >= T_s_B & T_s_A <= T_e_B

where T_s_A, T_s_B and T_e_B are the start time of events A and B, and the end time of event B, respectively.

Parameters:
  • events (Pandas.DataFrame) – The events data from a wind turbine. Must be free of NA values.
  • codes (list-like) – The event codes to look at
  • tsa_op1 (String (default ‘ge’)) – Operator to use for T_s_A >= T_s_B or T_s_A > T_s_B. Can be one of: ‘ge’: <= ‘gt’: <
  • tsa_op2 (String (default ‘le’)) – Operator to use for T_s_A <= T_e_B or T_s_A < T_e_B. Can be one of: ‘le’: >= ‘lt’: >
Returns:

trig_summary (Pandas.DataFrame) – A matrix consisting of the following:

  • parent_event: the event code of the parent event
  • parent_desc: description of the parent event
  • p_count: total number of parent events matching the event code
  • p_dur: total duration of parent events matching the event code
  • p_trig_count: number of parent events which have triggered child events
  • p_trig_dur: duration of parent events which have triggered child events
  • child_event: the event code of the child event
  • child_desc: description of the child event
  • c_count: total number of child events matching the event code
  • c_dur: total duration of child events matching the event code
  • c_trig_count: number of child events which have been triggered by parent events
  • c_trig_dur: duration of child events which have been triggered by parent events