1. A method for determining a crash condition of a vehicle comprising the steps of:
sensing crash acceleration in a first direction substantially parallel to a front-to-rear axis of the vehicle and providing a first acceleration signal indicative thereof;
sensing crash acceleration in a second direction substantially parallel to a side-to-side axis of the vehicle and near opposite sides of the vehicle and providing second acceleration signals indicative thereof;
determining a transverse crash value functionally related to the second acceleration signals;
comparing the determined transverse crash value against a safing threshold; and
determining a crash condition of the vehicle in response to (a) the comparison and (b) the first acceleration signal.
2. The method of claim 1 further comprising the step of providing an actuation signal for actuating an actuatable safety device of the vehicle in response to determining a crash condition of the vehicle.
3. The method of claim 1 wherein said step of determining a transverse crash value functionally related to the second acceleration signals includes the steps of:
determining moving averages of absolute values of acceleration in the second direction based on the second acceleration signals, and
determining the transverse crash value based on the determined moving averages of absolute values of acceleration in the second direction.
4. The method of claim 3 wherein said step of determining a transverse crash value functionally related to the second acceleration signals includes the step of determining a sum of the moving averages of absolute values of acceleration in the second direction based on the second acceleration signals, and wherein said step of determining the transverse crash value based on the determined moving averages of absolute values of acceleration in the second direction includes the step of determining the transverse crash value based on the determined sum of the moving averages of absolute values of acceleration in the second direction.
5. The method of claim 1 further comprising the steps of:
determining crash velocity in said first direction from the first acceleration signal;
determining crash displacement in said first direction from the first acceleration signal; and
comparing the determined crash velocity as a function of the determined crash displacement against one of a discrimination threshold and a switched discrimination threshold; and
wherein said step of determining a crash condition of the vehicle comprises determining a crash condition of the vehicle in response to both the determined crash velocity as a function of crash displacement exceeding one of the discrimination threshold and the switched discrimination threshold and the transverse crash value exceeding the safing threshold.
6. An apparatus for determining a crash condition of a vehicle, said apparatus comprising:
a first accelerometer for sensing crash acceleration in a first direction substantially parallel to a front-to-rear axis of the vehicle and providing a first acceleration signal indicative thereof;
second accelerometers for sensing crash acceleration in a second direction substantially parallel to a side-to-side axis of the vehicle and near opposite sides of the vehicle and providing second acceleration signals indicative thereof; and
a controller for determining a transverse crash value functionally related to the second acceleration signals and comparing the transverse crash value against a safing threshold, the controller also determining a crash condition of the vehicle in response to (a) the comparison and (b) the first acceleration signal.
7. The apparatus of claim 6 wherein the controller also provides an actuation signal for actuating an actuatable safety device of the vehicle in response to determining a crash condition of the vehicle.
8. The apparatus of claim 6 wherein the controller determines the transverse crash value from moving averages of absolute values of acceleration in the second direction based on the second acceleration signals.
9. The apparatus of claim 8 wherein the controller determines the transverse crash value as a sum of the moving averages of absolute values of acceleration in the second direction based on the second acceleration signals.
10. The apparatus of claim 6 wherein the controller further determines a crash velocity and a crash displacement from the first acceleration signal and compares the determined crash velocity as a function of the determined crash displacement against one of a discrimination threshold and a switched discrimination threshold, the controller determining a crash condition of the vehicle in response to both (a) the determined crash velocity as a function of the determined crash displacement exceeding one of the discrimination threshold and the switched discrimination threshold and (b) the transverse crash value exceeding the safing threshold.
11. The apparatus of claim 10 wherein at least one of the discrimination and transverse thresholds is a variable threshold.
12. The apparatus of claim 10 wherein at least one of the discrimination and transverse thresholds is a fixed threshold.
13. The apparatus of claim 6 wherein the first accelerometer is located at a substantially central vehicle location.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. An event analysis apparatus configured to analyze events including alarms generated in a plurality of devices and operations targeting the devices, comprising:
an event log collection unit configured to collect an event log including an occurrence date and time of the event, a device identifier (ID) of the device in which the event occurs, and an event type ID of the event;
an event log storage unit configured to convert the event log into an event matrix representing presence and absence of occurrence of each device event obtained by coupling the device ID and the event type ID in time series and save the event matrix; and
an event analysis unit configured to calculate a conditional probability between the device events to construct a Bayesian network by dividing the event matrix into blocks, each of which has a different predetermined reference time width and determining the presence and absence of the occurrence of each of the device events in each of the blocks, and decide a device event as a cause of a device event of an analysis target or a device event to be generated later using the constructed Bayesian network.
2. The event analysis apparatus according to claim 1, wherein the event log storage unit is configured to set the time series of the event matrix in a minimum time unit width of the event log.
3. The event analysis apparatus according to claim 1, wherein the event analysis unit is configured to calculate the conditional probability between the device events by calculating individual occurrence probabilities of the device events and a simultaneous occurrence probability between the device events based on the presence and absence of the occurrence of each of the device events in each of the blocks.
4. The event analysis apparatus according to claim 3, wherein the event analysis unit is configured to calculate the individual occurrence probability by setting the device event of a target, and dividing the number of the blocks in which the device event targeted has occurred by the total number of the blocks, and to calculate the simultaneous occurrence probability by setting a pair of the device events, and dividing the number of the blocks in which both the device events included in the pair have occurred by the total number of the blocks.
5. A non-transitory computer-readable storage medium storing a computer program for analyzing events including alarms generated in a plurality of devices and operations targeting the devices, wherein the computer program is executed to perform:
collecting an event log including an occurrence date and time of the event, a device ID of the device in which the event occurs, and an event type ID of the event;
converting the event log into an event matrix representing presence and absence of occurrence of each device event obtained by coupling the device ID and the event type ID in time series and saving the event matrix;
calculating a conditional probability between the device events to construct a Bayesian network by dividing the event matrix into blocks, each of which has a different predetermined reference time width and determining the presence and absence of the occurrence of each of the device events in each of the blocks, and
deciding a device event as a cause of a device event of an analysis target or a device event to be generated later using the constructed Bayesian network.
6. The non-transitory computer-readable storage medium according to claim 5, wherein the computer program is executed to perform setting the time series of the event matrix in a minimum time unit width of the event log.
7. The non-transitory computer-readable storage medium according to claim 5, wherein the computer program is executed to perform calculating the conditional probability between the device events by calculating individual occurrence probabilities of the device events and a simultaneous occurrence probability between the device events based on the presence and absence of the occurrence of each of the device events in each of the blocks.
8. The non-transitory computer-readable storage medium according to claim 7, wherein the computer program is executed calculating the individual occurrence probability by setting the device event of a target, and dividing the number of the blocks in which the device event targeted has occurred by the total number of the blocks, and calculating the simultaneous occurrence probability by setting a pair of the device events, and dividing the number of the blocks in which both the device events included in the pair have occurred by the total number of the blocks.
9. A method using a computer for analyzing events including alarms generated in a plurality of devices and operations targeting the devices, comprising:
collecting an event log including an occurrence date and time of the event, a device ID of the device in which the event occurs, and an event type ID of the event;
converting the event log into an event matrix representing presence and absence of occurrence of each device event obtained by coupling the device ID and the event type ID in time series and saving the event matrix;
calculating a conditional probability between the device events to construct a Bayesian network by dividing the event matrix into blocks, each of which has a different predetermined reference time width and determining the presence and absence of the occurrence of each of the device events in each of the blocks; and
deciding a device event as a cause of a device event of an analysis target or a device event to be generated later using the constructed Bayesian network.
10. The method according to claim 9, wherein converting the event log into the event matrix and saving the event matrix comprises setting the time series of the event matrix in a minimum time unit width of the event log.
11. The method according to claim 9, wherein calculating the conditional probability to construct the Bayesian network comprises calculating the conditional probability between the device events by calculating individual occurrence probabilities of the device events and a simultaneous occurrence probability between the device events based on the presence and absence of the occurrence of each of the device events in each of the blocks.
12. The method according to claim 11, wherein calculating the conditional probability to construct the Bayesian network comprises calculating the individual occurrence probability by setting the device event of a target, and dividing the number of the blocks in which the device event targeted has occurred by the total number of the blocks, and calculating the simultaneous occurrence probability by setting a pair of the device events, and dividing the number of the blocks in which both the device events included in the pair have occurred by the total number of the blocks.