JP6673737B2 - System device abnormality diagnosis device, elevator abnormality diagnosis device, and elevator abnormality diagnosis method - Google Patents

Description

  The present invention relates to a system device abnormality diagnosis device, an elevator abnormality diagnosis device, and an elevator abnormality diagnosis method, and more particularly to a system device abnormality diagnosis device, an elevator abnormality diagnosis device, and an elevator abnormality that can quickly identify the cause of an abnormal state. It relates to a diagnostic method.

  2. Description of the Related Art An elevator as a system device transports users and luggage by a car that moves up and down a hoistway formed in a building. In particular, many elevators are installed in large-scale buildings and the like, and many users use the elevators. In recent elevators, a user himself / herself operates a landing button or a car button to call the car and operate the car on the destination floor.

  For example, when a user gets on an elevator car, pressing a call button provided at a landing (elevator hall) on each floor, the passenger arrives at the landing where the car was called in response to the call button, and then returns to the landing. The door of the car and the car door are automatically opened, and the user can get into the car. In addition, when the user who gets on the car presses the destination button in the car and specifies the target floor, the door of the landing and the door of the car closes automatically, and the car turns to the target floor. To move up and down. Upon arriving at the target floor, the landing door and the car door are automatically opened, and the user moves from the car to the landing.

  As described above, in a large-scale building or the like, a plurality of elevators are installed, and since many users use the elevators, it is extremely important to operate and manage the elevators in a normal state. Therefore, in order to operate and manage the elevator in a normal state, a remote monitoring device that remotely monitors the operating state of the elevator is used.

  The remote monitoring device monitors various abnormal states that occur during the operation of the elevator. For this reason, the remote monitoring device is provided with an abnormality diagnosis device that performs an abnormality diagnosis corresponding to the type of the abnormal state. This abnormality diagnosis device captures detection signals of various sensors provided in an elevator and executes abnormality diagnosis using a predetermined abnormality diagnosis program.

  For example, Japanese Patent Application Laid-Open No. 2006-143359 (Patent Document 1) aims to diagnose the operating state of an elevator installed in a building to be diagnosed based on the operation history of elevators installed in a plurality of buildings. Then, the following abnormality diagnosis device has been proposed.

  This abnormality diagnosis apparatus includes an installation data storage unit that stores installation data related to an elevator, a history data storage unit that stores elevator operation history data, and a similar building identification unit that identifies a similar building from a plurality of buildings (diagnosis target building data Extraction section, search condition creation section, history data extraction section) and a diagnosis section for diagnosing the operating state of the elevator installed in the building to be diagnosed. The relative evaluation is performed to diagnose the operating state of the elevator.

  It should be noted that many other abnormality diagnosis devices for the remote monitoring device have been proposed, and the above-mentioned Patent Document 1 shows one example. The remote monitoring device is also in data communication with the control device installed in the car, and the control device sends output information from various sensors and elevator control information to the remote monitoring device, which responds to abnormal conditions. Backup information and notification information to be sent to the control device. Further, information on the landing is also sent to the remote monitoring device, and is used for operation and management of the elevator.

JP 2006-143359 A

  In the control device installed in the elevator car, for example, the presence or absence of a user, the time at which the user got into the car, etc. are detected by various sensors provided in the platform and the car and grasped as log information. Have been. For example, an image of a sensor camera attached to a car, a door sensor, and a load sensor can detect a user's entry / exit time and presence / absence of a user. It is to be noted that information on the user can be detected by using sensors other than these, and various information other than the presence / absence of the user and the entry / exit time can also be detected.

  When the presence of the user is confirmed, the doors that normally close after a predetermined time (the landing and the door of the car) are maintained in an open state so that users entering and exiting the car are not pinched by the doors. I have to. Also, in the state where the user suddenly enters and exits in the process of closing the door, the operation of opening the door that is being closed is performed again. In this way, at the elevator landing and the car, using various sensors, the behavior of the user is monitored and the operation of the door is controlled so that the user is not pinched by the door. .

  By the way, the event that the user collides with the door due to the time delay from the detection of the user by various sensors until the door is reopened, and the user forcibly passes through the closing door, An event that collides with a door, or an event that a user jokes and jumps in a car and collides with a door, often occurs. If the user collides with the door and exerts a large force on the door, the opening and closing of the door will have a considerable adverse effect.

  In many cases, even if the user collides with the door, the door has almost no effect, but in some cases, the door may temporarily not operate smoothly. That is, a temporary abnormal state based on an artificial cause may occur. In such a state, since the switches and sensors that detect the open / closed state of the door detect that the open / closed state of the door is different from normal, the abnormality diagnosis device determines that an abnormality has occurred. An alert is issued to the user, and in some cases, an operation of stopping the operation of the elevator is performed to ensure safety.

  When such an abnormal condition occurs, the maintenance worker of the operator who operates and manages the elevator goes to the site where the elevator that caused the abnormal condition is located, checks the abnormal condition, and identifies the cause of the abnormal condition. After the cancellation, the elevator has been restored. However, before the maintenance worker arrives at the site, the door may return to a normal state for some reason (for example, when the user corrects the distortion or the position of the door). Therefore, in such a state, when the maintenance worker arrives at the site, the abnormal state has been resolved, and it takes much time to find out the true cause determined to be the abnormal state.

  As a similar example, for example, when a foreign object is placed on a rail portion or a driving portion of a door (including intentional or unintentional cases), the abnormality diagnosis device determines that the door is not completely closed. It is determined that an abnormal state has occurred in the door. That is, a temporary abnormal state based on an artificial cause occurs. However, if a user or a staff member of a building maintenance company removes foreign matter, the door returns to operate normally.

  Even in such a state, since the abnormal state has been resolved when the maintenance worker arrives at the site, it takes much time to find out the cause of the determination as the abnormal state. The occurrence of a temporary abnormal state based on an artificial cause is caused by many causes other than the example described above.

  On the other hand, apart from an abnormal state due to a human cause, an abnormal state occurs in an element device constituting an elevator due to a length of time used and various physical factors (mechanical and electrical factors). For example, a relatively large number of abnormal states of the element devices due to the deterioration over time occur. This abnormal state is detected by various sensors, but the output information of the various sensors cannot determine whether it is an abnormal state based on an artificial cause or an abnormal state based on a cause of an element device.

  In order to determine the cause of the abnormal condition, the maintenance worker must stop the elevator and investigate, irrespective of the abnormal condition caused by an artificial cause or the abnormal condition caused by the element device. However, during the survey period by maintenance workers, the elevator cannot be used. Therefore, in order to improve the convenience for the user, it is desirable to identify the cause of the abnormal state at an early stage.

  The same problem may occur not only in elevators but also in various system devices. Therefore, in the embodiment described below, an example of an elevator will be described as a typical example, but the present invention is also applicable to other system devices. Therefore, in the following, the elevator including these components can be read as a system device.

  An object of the present invention is that an abnormal state generated in an elevator (system equipment) is an abnormal state generated by an artificial cause or an abnormal state generated by a factor based on an element device constituting the elevator (system equipment). It is an object of the present invention to provide a new elevator abnormality diagnosis device (system device abnormality diagnosis device) and an elevator abnormality diagnosis method, which can identify the condition of the elevator at an early stage.

  The feature of the present invention is that the output information of the behavior sensor for acquiring information on the behavior of the user of the elevator to be diagnosed (diagnosed system equipment) to be diagnosed is the same as the behavior sensor of the elevator to be diagnosed (diagnosed system equipment). Then, by comparing the output information of the behavior sensor of the first comparison elevator (first comparison system device) having a similar business type and application, the behavior information of the user of the diagnosed elevator (diagnosis system device) is obtained. , The output information of the element device sensor for acquiring information on the element devices constituting the elevator to be diagnosed (system device to be diagnosed), and the elevator device to be diagnosed ( Output of elementary device sensors of the second comparison elevator (second comparison system device) of the same model as the system device) By comparing the information with the information to obtain the variation information on the operation of the component devices of the elevator to be diagnosed (diagnosed system device), the correlation between the behavior information of the user and the variation information of the component devices allows the elevator to be diagnosed (system components to be diagnosed) to be determined. ) To determine whether an abnormal condition has occurred due to an artificial cause.

  According to the present invention, the abnormal state occurring in the elevator to be diagnosed (diagnosed system device) is an abnormal state caused by an artificial cause or based on the element device of the elevator to be diagnosed (diagnosed system device). It is possible to identify at an early stage whether the state is an abnormal state caused by the cause, and it is possible to shorten the stop period of the elevator to be diagnosed (diagnosed system device) and improve the convenience for the user.

1 is a configuration diagram of an elevator abnormality diagnosis device according to an embodiment of the present invention. FIG. 9 is an explanatory diagram illustrating output information of a sensor regarding a user and output information of a sensor regarding an element device, and illustrating a case where the behavior of the user is normal. FIG. 9 is an explanatory diagram illustrating output information of a sensor regarding a user and output information of a sensor regarding an element device, and illustrating a case where the behavior of the user is abnormal. FIG. 9 is an explanatory diagram illustrating a method of specifying an abnormal state based on a correlation between output information of an abnormal operation detection unit and a fluctuation information detection unit that are behavior detection units. FIG. 6 is a configuration diagram of an abnormality diagnosis device for an elevator according to another embodiment of the present invention.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. However, the present invention is not limited to the following embodiments, and various modifications and application examples are included in the technical concept of the present invention. Is included in the range. As described above, the present invention is not limited to an elevator, and can be applied to various system devices. Hereinafter, an elevator will be described as a typical example.

  FIG. 1 shows the configuration of an elevator abnormality diagnosis device according to an embodiment of the present invention. In FIG. 1, a passenger (not shown) of an elevator EL1 to be diagnosed (hereinafter referred to as a diagnosed elevator) EL1 has a person for determining whether or not a user is present in a door opening / closing area. A sense sensor 1, an image sensor 2 such as a monitoring camera or a sensor camera provided in the car of the elevator EL1 to be diagnosed, and a load sensor for measuring the weight of the user and the luggage who have entered the car of the elevator EL1 to be diagnosed. 3 are provided. Of course, it is also possible to provide other sensors, such as a door sensor 4 for detecting the opening and closing of the door, and other sensors A5 and B6.

  Then, the output information of the motion sensor 1, the image sensor 2, and the load sensor 3, which are the behavior sensors for detecting the behavior of the user, is transmitted to the abnormal operation detection means 7 as the behavior detection means as the behavior information of the user. Has been entered. (Hereinafter, the behavior detecting means is referred to as an abnormal operation detecting means 7.) The output information input to the abnormal operation detecting means 7 is treated as behavior information indicating a user's behavior and the like. It should be noted that the output information of the behavior sensor representing the behavior information of the user can be used in addition to the above-described sensors. In short, any information that represents the behavior of the user may be used. The reason for using such behavior information of the user is to determine whether or not an artificial abnormal state has been caused by the user.

  Further, the comparison behavior information is input to the abnormal operation detection means 7, and this comparison behavior information forms a threshold value to be compared with the behavior information of the elevator EL1 to be diagnosed. The comparison behavior information is obtained by the first first comparison elevator EL2. The first comparative elevator EL2 is an elevator different from the elevator EL1 to be diagnosed, and is an elevator having a similar business type and application in which the elevator EL1 to be diagnosed is used.

  For example, if the elevator EL1 to be diagnosed is an elevator of a department store, the first comparison elevator EL2 is also an elevator of a department store. If the elevator EL1 to be diagnosed is the elevator of the office building, the first comparison elevator EL2 is also the elevator of the office building. In addition, there are other elevators having similar business types and uses, but description thereof is omitted here.

  The reason for using the comparative elevators having similar business conditions and uses in this way is that the user of the elevator EL1 to be diagnosed and the user of the first comparative elevator EL2 can be regarded as having a high probability of performing the same behavior. Because.

  In the sensor group 8 of the first comparison elevator EL2, the same human sensor, image sensor, and load sensor as the human sensor 1, the image sensor 2, and the load sensor 3 used in the diagnosed elevator EL1 are used. The output information is input to the normal behavior information storage means 9 as normal behavior information. The normal behavior information storage means 9 is inputted every time a predetermined time elapses. The normal behavior information storage means 9 may directly input the output information of the motion sensor, the image sensor, and the load sensor to the abnormal operation detection means 7 as comparison behavior information, or may calculate the output information of each sensor by a predetermined calculation. May be input to the abnormal operation detecting means 7 as the comparison behavior information.

  In the abnormal operation detecting means 7, the behavior information is formed by the information from the motion sensor 1, the image sensor 2, and the load sensor 3 of the elevator EL1 to be diagnosed, and the behavior information of the elevator EL1 to be diagnosed is the first comparison elevator. This is compared with comparative behavior information in a predetermined range formed by information from the motion sensor, image sensor, and load sensor of EL2. For example, if the behavior information of the elevator EL1 to be diagnosed falls within a predetermined range with respect to the behavior information for comparison of the first elevator EL2, the behavior information of the elevator EL1 to be diagnosed is set to a normal state and exceeds the predetermined range. If it has fluctuated, it is assumed that the fluctuation of the behavior information of the elevator EL1 to be diagnosed is suspected to have been caused by an artificial cause.

  In the present embodiment, the "probability" that an abnormal state has occurred due to an artificial cause according to the degree to which the value of the behavior information of the elevator EL1 to be diagnosed exceeds the value of the behavior information for comparison. Is determined to be high. That is, as the value of the behavior information of the elevator EL1 to be diagnosed becomes larger than the average value (or the median value of the predetermined range) of the comparison behavior information of the first comparison elevator EL2, the abnormal state due to an artificial cause is increased. Is set so that the “probability” at which the error occurs is increased.

  On the other hand, output information of, for example, a human sensor 1, an image sensor 2, a load sensor 3, a door sensor 4, a sensor A5, and a sensor B6, which are element device sensors of the elevator EL1 to be diagnosed, are input to the fluctuation information detecting means 10. ing. Note that the element device sensor and the behavior sensor include the same sensor. The output information input to the fluctuation information detecting means 10 is treated as temporal deterioration information indicating the temporal deterioration (causes based on the element devices) of the element devices constituting the elevator EL1 to be diagnosed. Further, the aging deterioration information can be regarded as fluctuation information relating to the operation of the element device.

  It should be noted that the output information of the element device sensor representing the aging deterioration information can be used in addition to the above-described sensors, and it is essential that the output information is information representing the aging deterioration of the element device. However, in the present embodiment, the abnormal state of the element device is described using the fluctuation information due to aging as the fluctuation information of the element device, but other causes such as mechanical and electrical effects are caused. It goes without saying that an abnormal state of the element device may be determined using the fluctuation information on the operation of the element device.

  Here, the element devices are, for example, a door constituting a car, a door opening / closing mechanism, a door drive motor, a hoisting machine for winding up the car, a braking device, and the like. The following describes an example of a device related to this.

  Further, the comparison aging information is input to the fluctuation information detection means 10, and this comparison aging information forms a threshold value to be compared with the aging deterioration information of the elevator EL1 to be diagnosed. The comparison aging information is obtained by the second comparison elevator EL3. The second comparison elevator EL3 is an elevator different from the diagnosed elevator EL1, and is an elevator of the same model (same model) as the diagnosed elevator EL1.

  As described above, the elevator of the same type as the elevator to be diagnosed is used as the second elevator for comparison EL3 because the elevators EL1 to be diagnosed and the elevator EL3 for second comparison have the same element devices, and therefore have the same elements as each other. This is because it can be considered that there is a high probability that the tendency of the device over time is the same.

  Then, in the sensor group 11 of the second comparative elevator EL3, the same as the human sensor 1, the image sensor 2, the load sensor 3, the door sensor 4, the sensor A5, and the sensor B6 used in the elevator EL1 to be diagnosed. Each output information of the human sensor, the image sensor, the load sensor, the door sensor, the sensor A, and the sensor B is input to the temporal deterioration information storage unit 12 as a change with the passage of time (deterioration over time).

  The temporal deterioration information storage means 12 is inputted every time a predetermined time elapses. The aging information storage unit 12 generates comparison aging information from each output information of the human sensor, the image sensor, the load sensor, the door sensor, the sensor A, and the sensor B, and is directly input to the fluctuation information detecting unit 10. ing.

  For example, whether or not deterioration with time has occurred can be determined by, for example, obtaining the amount of change (deviation) in output information at predetermined time intervals under the same environmental conditions and using the information with time to determine the degree of deterioration with time. You can ask. Other than this, the tendency of deterioration with time can be grasped by various methods, but it is only necessary to adopt an appropriate method for the system.

  In the fluctuation information detecting means 10, the aging deterioration information is formed by the information from the various sensors of the elevator EL1 to be diagnosed, and the aging deterioration information of the elevator EL1 to be diagnosed is obtained by the information from the various sensors of the second comparison elevator EL3. The information is compared with the formed aging information for comparison in a predetermined range. For example, if the time-lapse deterioration information of the elevator EL1 to be diagnosed falls within a predetermined range with respect to the time-lapse deterioration information for comparison of the second comparison elevator EL3, the time-lapse deterioration information of the elevator EL1 to be diagnosed is set to a normal state. If it fluctuates beyond the range, it is regarded that there is a suspicion that the fluctuation of the temporal deterioration information of the elevator EL1 to be diagnosed has been caused by the deterioration of the element device.

  Note that, in the present embodiment, an abnormal state occurs due to the deterioration of the element device in accordance with the degree to which the value of the aging degradation information of the elevator EL1 to be diagnosed exceeds the value of the aging degradation information for comparison. We judge that "certainty" is high. That is, as the value of the aging degradation information of the elevator EL1 to be diagnosed becomes larger than the average value (or the median of the predetermined range) of the aging degradation information for comparison of the second comparison elevator EL3, the deterioration of the element devices is increased. The "probability" that an abnormal state has occurred due to the cause is set as high.

  The behavior information on the behavior of the user determined by the abnormal operation detecting means 6 and the aging information by the fluctuation information detecting means 10 are input to the cause determining means 13. The cause determining means 13 determines whether the fluctuation of the output information of each sensor is due to the deterioration over time of the element device or to the abnormal behavior of the user.

  Based on the correlation between the behavior information on the behavior of the user and the aging information of the element devices, the cause determination unit 13 determines whether the abnormal state that has occurred in the elevator EL1 to be diagnosed is an abnormal state that has occurred due to an artificial cause, or This is for determining whether or not an abnormal state has occurred due to a factor based on the component devices of the diagnostic elevator. The determination of the abnormal state based on the correlation will be described with reference to FIG.

  When the cause determination means 13 determines an abnormal state, the result of the determination is sent to the output device 14. The output unit 14 notifies at least the determination result of the cause determination unit 13 by a display screen or sound, but can also display the output information of the abnormal operation detection unit 7 and the fluctuation information detection unit 10 in time series. It is configured as follows. In this case, the output information of the abnormal operation detecting means 6 and the fluctuation information detecting means 10 may be stored in time series at every predetermined time.

  Next, specific processing of the abnormal operation detecting means 6, the fluctuation information detecting means 10, and the cause determining means 13 will be described. FIG. 2 shows output information of the sensor relating to the behavior of the user and output information of the sensor relating to the aging of the element devices, and illustrates a case where the behavior of the user is normal. FIG. 3 shows the behavior of the user. It shows the output information of the sensor and the output information of the sensor regarding the aging of the element devices, and explains the case where the behavior of the user is abnormal. FIG. 4 illustrates a method of specifying an abnormal state based on the correlation between the output information of the abnormal operation detecting means 6 and the output information of the fluctuation information detecting means 10.

  FIG. 2 shows the output information of the sensor relating to the behavior of the user and the output information of the sensor relating to the aging of the element devices as described above, and explains the case where the behavior of the user is normal.

  In FIG. 2, the output characteristic shown on the upper side indicates a fluctuation state of output information (load) of the load sensor 3 provided in the car of the elevator EL1 for diagnosis. The load sensor 3 is used to determine whether an abnormal state has occurred due to an artificial cause. Time t0 to time t1, time t2 to time t3, and time t4 to time t5 indicate a period from when the car stops on a predetermined floor to when the car opens and closes again, and between time t1 and time t2. , Time t3 to time t4, and time t5 indicate an operation period during which the car is operating and moving on the hoistway.

  Then, at time t0 to time t1, time t2 to time t3, and time t4 to time t5, the door of the car is opened and closed, and the user gets on and off, and finally the number of users existing in the car is the total weight. It is measured by the load sensor 3. In this state, the total weight of the same number of persons in the first comparison elevator EL2 is used as a threshold for comparison, and is compared with the output information of the load sensor 3 of the diagnosis target elevator EL1 by the abnormal operation detecting means 7. The output information of the load sensor 3 of the first comparison elevator EL2 may be an average value of the output information of the load sensors 3 of the plurality of first comparison elevators EL2.

  The threshold value of the load sensor of the first comparison elevator EL2 has a predetermined range, and if the output information of the load sensor 3 of the elevator EL1 for diagnosis falls within this range, the abnormal operation detecting means 7 is used. Have no abnormal behavior. The abnormal behavior will be described with reference to FIG. In FIG. 2, during the operation period from time t1 to time t2, it falls within the range of the threshold value WSL1, and also during the operation period from time t3 to time t4, it falls within the range of the threshold value WSL2. Is assumed not to have occurred.

  Here, in the present embodiment, the load sensor 3 is used as the behavior sensor, but the sensor camera 2 can be used instead. In this case, it is possible to detect an abnormality in the behavior of the user by measuring the difference in the operation position, the operation speed, and the operation time of the user in the image. Further, it is possible to detect an abnormality in the behavior of the user based on the operation pattern of the user and the change history of the pattern.

  Next, the output characteristics shown on the lower side show the fluctuation state of the output information of the door sensor 4 provided in the car of the elevator EL1 for diagnosis. The door sensor 4 detects the opening / closing speed of the car door, which is a component device of the elevator EL1 for diagnosis, and the temporal fluctuation of the opening / closing speed causes an abnormal state of the car door due to aging. It is determined whether or not it is. Here, the opening / closing speed of the door is obtained from the average speed of both when the door changes from the closed state to the open state and when the door changes from the open state to the closed state.

  Then, at time t0 to time t1, time t2 to time t3, and time t4 to time t5, the average speed is calculated both when the door changes from the closed state to the open state and when the door changes from the open state to the closed state. doing. Then, a difference value ΔV (= deterioration deterioration information) between the average opening / closing speed measured at a predetermined time and the average opening / closing speed measured at a predetermined time after a predetermined time has elapsed is calculated. It is considered that the influence of the deterioration over time is large and the possibility of an abnormal state is high. Of course, the same operation is executed and stored in the second comparison elevator EL3 in response to the opening and closing of the car door.

  In this state, the difference value ΔVSL of the average opening / closing speed of the door obtained by the door sensor 4 of the second comparison elevator EL3 (the elapsed time at the predetermined time before and after obtaining the difference value is the same time) is for comparison. It is used as a threshold value and is compared with the difference value ΔV of the average opening / closing speed of the door obtained by the door sensor 4 of the elevator EL1 for diagnosis by the fluctuation information detecting means 10. Since the second comparative elevator EL3 is of the same model as the elevator EL1 to be diagnosed, the degree of progress of the temporal deterioration shows almost the same tendency. Therefore, by comparing the difference values between the two, the abnormal state based on the temporal deterioration is estimated. be able to.

  When selecting the second comparison elevator EL3, it is desirable to select the one with the latest installation date and time as the second comparison elevator EL3. Thus, the probability of detecting the deterioration with time of the elevator EL1 for diagnosis is increased. Further, as the aging deterioration information (difference value) of the door sensor of the second comparison elevator EL3, an average value of the aging deterioration information of the door sensors 3 of the plurality of second comparison elevators EL3 may be used.

  The threshold value (difference value ΔVSL) of the door sensor of the second comparison elevator EL3 has a predetermined range, and the temporal deterioration information (difference value) of the door sensor 4 of the elevator EL1 for diagnosis falls within the predetermined range. If so, the fluctuation information detecting means 10 determines that the door, which is an element device, does not have an abnormal state due to aging. In the example shown in FIG. 2, a difference value of ΔV1 occurs between the average opening / closing speed from time t0 to time t1 and the average opening / closing speed from time t2 to time t3, and similarly, the average opening / closing speed from time t0 to time t1. A difference value of [Delta] V2 is generated between the speed and the average opening / closing speed from time t4 to time t5, and the door gradually becomes difficult to close due to deterioration with time.

  Therefore, from the threshold value (difference value ΔVSL) within a predetermined range determined by the aging deterioration information (difference value) of the door sensor of the second comparison elevator EL3, the aging deterioration information (difference value) ΔV1, of the door sensor 3 of the elevator EL1 to be diagnosed, Alternatively, when the value of ΔV2 has exceeded, it can be estimated that an abnormal state has occurred or is likely to occur in the door, which is an element device. Here, it is set so that the larger the difference value is, the more the deterioration of the door with time progresses, and the higher the “probability” that an abnormal state occurs in the door.

  Then, based on the behavior information of the user of the abnormal operation detecting means 7 and the aging deterioration information of the fluctuation information detecting means 10, the cause determining means 13 determines that the abnormal state generated in the elevator is an abnormal state generated due to an artificial cause. It is possible to identify at an early stage whether or not there is an abnormal state caused by a factor based on element devices constituting the elevator. Since the abnormal state shown in FIG. 2 is not considered to be an abnormal state caused by an artificial cause by the abnormal operation detecting means 7, the cause determining means 13 determines that the abnormal state Is determined. This determination result can be easily confirmed by the output device 14.

  Next, a case where the user performs abnormal behavior will be described. FIG. 3 shows the output information of the sensor relating to the behavior of the user and the output information of the sensor relating to the deterioration of the element devices as described above, and explains the case where the behavior of the user is abnormal.

  In FIG. 3, the output characteristics shown on the upper side show the fluctuation state of the output information (load) of the load sensor 3 provided in the car of the elevator EL1 for diagnosis, similarly to FIG. 2. The load sensor 3 is used to determine whether an abnormal state has occurred due to an artificial cause as described above. The time t0 to the time t1, the time t2 to the time t3, the time t4 to the time t5, and the time t6 to the time t7 indicate a period from when the car stops on a predetermined floor to when the car opens and closes again. , A time t1 to a time t2, a time t3 to a time t4, a time t5 to a time t6, and a time t7 to indicate an operation period during which the car is operating and moving on the hoistway.

  Then, at time t0 to time t1, time t2 to time t3, time t4 to time t5, and time t6 to time t7, the user gets on and off, and the total number of users who are present in the car finally becomes the total weight. It is measured by the load sensor 3. In this state, the total weight of the same number of persons in the first comparison elevator EL2 is used as a threshold for comparison, and is compared with the output information of the load sensor 3 of the diagnosis target elevator EL1 by the abnormal operation detecting means 7. This state is the same as the case shown in FIG.

  However, if the user performs an abnormal behavior such as jumping in the car during the operation period from time t3 to time t4, the load sensor 3 detects large output information (load) Abw in response to this. . This output information Abw is larger than the threshold value WSL2 of the output information (load) of the load sensor 3 of the first comparison elevator EL2. Therefore, the abnormal operation detecting means 7 regards that an abnormal behavior of the user has occurred during the operation period from the time t3 to the time t4, and considers that an abnormal state due to an artificial cause has occurred. As described above, as the size of the output information Abw from the load sensor 3 increases, the “certainty” of the determination that the abnormal behavior of the user has occurred increases.

  As described above, since the first comparison elevator EL2 similar in business type and application is used for the diagnosis elevator EL1, the user of the diagnosis elevator EL1 and the first comparison elevator EL2 are generally used. The probability that the user performs the same behavior is high. For this reason, the rate at which the abnormal state is normally detected by the user is low, but the abnormal state is determined by the user who is different from the user of the elevator EL1 for diagnosis and the first comparative elevator EL2 having a similar business type and application. If there is such a behavior, it is considered that an abnormal state has occurred by the user.

  Next, the output characteristic shown on the lower side also shows the fluctuation state of the temporal deterioration information of the door sensor 4 provided in the car of the elevator EL1 for diagnosis, similarly to FIG. The door sensor 4 detects the opening / closing speed of the car door, which is a component device of the elevator EL1 for diagnosis, and the temporal fluctuation of the opening / closing speed causes an abnormal state of the car door due to aging. It is determined whether or not it is. Here, the opening / closing speed of the door is calculated from the average speed of both when the door changes from the closed state to the open state and when the door changes from the open state to the closed state.

  At time t0 to time t1, time t2 to time t3, time t4 to time t5, and time t6 to time t7, when the door changes from the closed state to the open state, and when the door changes from the open state to the closed state. Both average speeds are calculated. Then, a difference value between the average opening / closing speed measured at a predetermined time and the average opening / closing speed measured at a predetermined time after a predetermined time has elapsed is determined. It is considered that the possibility of an abnormal state is high.

  In this state, the difference value of the average opening / closing speed of the doors obtained by the door sensor 4 of the second comparison elevator EL3 (the elapsed time at a predetermined time before and after obtaining the difference value is the same time) is a threshold value for comparison. And compared with the aging information (difference value) of the door sensor 4 of the elevator EL1 for diagnosis by the fluctuation information detecting means 10. This state is the same as the case shown in FIG. However, in the case of FIG. 3, it is assumed that there is no abnormality due to aging. Therefore, there is almost no difference between the average opening / closing speed between time t0 and time t1 and the average opening / closing speed between time t2 and time t3, and there is no occurrence of an abnormal state due to aging.

  However, during the operation period from the time t3 to the time t4, the user performs an abnormal behavior such as jumping in the car, and the user may give a large impact to the door of the car. For this reason, the door will be in a state different from the normal mounted state, and will interfere with its operation. Therefore, after the operation period from time t3 to time t4 when the user has abnormal behavior, the average opening and closing speed of the door from time t4 to time 5 and the average opening and closing speed from time t0 to time t1 are between The difference value of ΔV3 is generated, and it can be determined that the door is hard to close due to the abnormal behavior of the user.

  Therefore, when the abnormal output information Abw by the load sensor 3 is detected, the diagnosis is performed based on the threshold value (difference value ΔVSL) in a predetermined range determined by the aging deterioration information (difference value) of the door sensor of the second comparison elevator EL3. If the time-dependent deterioration information (difference value) ΔV3 of the door sensor 3 of the elevator EL1 is exceeded, it can be determined that an abnormal state has occurred or is likely to occur in the door due to an artificial cause.

  Then, based on the behavior information of the user of the abnormal operation detecting means 7 and the aging deterioration information of the fluctuation information detecting means 10, the cause determining means 13 determines that the abnormal state generated in the elevator is an abnormal state generated due to an artificial cause. It is possible to identify at an early stage whether the state is an abnormal state caused by a factor based on an element device constituting the elevator. Since the abnormal state shown in FIG. 3 is regarded as an abnormal state caused by an artificial cause by the abnormal operation detecting means 7, the cause determining means 13 determines that the abnormal state is based on the artificial cause. Execute the judgment to be considered. This determination result can be easily confirmed by the output device 14.

  The average opening / closing speed of the door from time t6 to time t7 has a difference value of ΔV4 between the average opening / closing speed from time t0 to time t1. This is considered that the door sensor 4 has deteriorated with time when viewed from the relationship between the output information (load) of the load sensor 3 and the average opening / closing speed of the door as shown in FIG.

  However, actually, in this example, no abnormality due to deterioration with time has occurred, so the change in the average opening / closing speed of the door between time t6 and time t7 is based on an artificial cause that has occurred during the operation period between time t3 and time t4. Since it is known that the state is an abnormal state, using this information, the change in the average opening / closing speed of the door from time t6 to time t7 is not a deterioration over time but an abnormal state based on an artificial cause. Can be determined.

  Here, the cause determination means 13 may employ the following determination method. In other words, when an abnormality of an element device is detected within a predetermined time after detecting an abnormal behavior of a user, if the abnormality of the element device at this time is an abnormal state caused by an artificial cause, the It is possible to further increase the probability of determining an abnormal state. Further, if the abnormal state of the element device is detected after the lapse of the predetermined time, this abnormal state can be determined as a true abnormal state of the element device.

  Next, a description will be given of a method in which the cause determination unit 13 specifies whether the state is an abnormal state caused by an artificial cause or an abnormal state caused by a factor based on an element device included in the elevator. In FIG. 4, the horizontal axis indicates the fluctuation information of the fluctuation information detecting means 10, and the vertical axis indicates the behavior information of the abnormal operation detecting means 7.

  As described above, as the behavior information (load difference value) of the abnormal operation detecting means 7 is larger, the “certainty” that the abnormal state has occurred due to an artificial cause is set higher. Similarly, as the temporal deterioration information (difference value of the opening / closing speed) of the fluctuation information detecting means 10 increases, the “certainty” that the abnormal state has occurred due to the temporal deterioration of the element device is set higher.

  "Black circles" in the figure indicate the correlation between the behavior information of the abnormal operation detecting means 7 and the aging information of the fluctuation information detecting means 10, respectively. The value of a certain aging information of the fluctuation information detecting means 10 at the same time is shown. 5 shows the value of the behavior information of the abnormal operation detecting means 7 at the time of. A range surrounded by a broken line indicates a range where each correlation is recognized, and these correlations are exemplarily shown as “state A” to “state D”.

  In FIG. 4, the fluctuation information has a region partition point P. If the variation information is smaller than the region partition point P, it is considered that the deterioration with time is small and no abnormal state occurs. A condition is considered to occur. Similarly, the behavior information has an area section point Q. If the area information is smaller than the area section point Q, it is considered that an abnormal state does not occur due to artificial behavior by the user. It is considered that an abnormal state occurs due to artificial abnormal behavior caused by the above.

  Then, four regions are defined and formed by the region partition point P and the region partition point Q. (1) The Nr region which is a normal region in which the variation information is smaller than the region partition point P and the abnormal behavior information is smaller than the region partition point Q. (2) Abn1 area which is an abnormal area where the fluctuation information is larger than the area division point P and abnormal behavior information is smaller than the area division point Q; and (3) the abnormality information is smaller than the area division point P and the abnormal behavior information Abn2 region, which is an abnormal region, is larger than the region partition point Q, and Abn3 region, which is an abnormal region, where (4) variation information is larger than the region partition point P, and abnormal behavior information is larger than the region partition point Q. ing.

  The “state A” belongs to the Nr area, and is a state in which the elevator EL1 to be diagnosed is used normally, and both the abnormal behavior information and the fluctuation information have a predetermined value equal to or less than the area division points P and Q. Has become. This is the state from time t0 to time t1 in FIGS. 2 and 3, for example.

  In contrast to the "state A", the "state B" belongs to the Abn1 area, and the fluctuation information is larger than the area division point P and the abnormal behavior information is smaller than the area division point Q. This “state B” is a result of the deterioration with time shown in FIG. 2 and is represented by a transition from “state A” to “state B”. However, in this case, there is no transition from “state B” to “state A”, and it is determined that this is a true abnormal state.

  In contrast to “state A”, “state C” belongs to the Abn2 area, and the fluctuation information is smaller than the area division point P and the abnormal behavior information is larger than the area division point Q. The “state C” is a result of the occurrence of an abnormal state due to the artificial behavior shown in FIG. 3, and is represented by a transition from “state A” to “state C”. In this state, no deterioration with time has occurred. By shifting from the “state A” to the “state C”, it can be determined that an abnormal behavior has been performed by the user. In this case, if the abnormal state due to the artificial behavior is eliminated, the state changes from “state C” to “state A”.

  In contrast to “state A”, “state D” belongs to the Abn3 area, and the fluctuation information is larger than the area division point P and the abnormal behavior information is larger than the area division point Q. In this “state D”, an abnormal state occurs due to the artificial behavior shown in FIG. 3, and further, an abnormal state of deterioration with time occurs due to the artificial behavior. From “state A” to “state D” It is expressed by transitioning to. In this state, it is determined that an abnormal state has occurred due to an artificial behavior, and thus it is considered that no temporal deterioration has occurred. By shifting from “state A” to “state D”, an abnormal behavior is performed by the user, and as a result, it can be determined that temporal deterioration has occurred temporarily. Note that “state D” changes to “state A” or “state B” when the artificial abnormal state is resolved.

  Then, in FIG. 4, in the “state B” and the “state D”, the fluctuation information indicates a value different from the normal state, so that it is determined that the state requires appropriate inspection and maintenance. However, in the “state D”, since the output of the behavior information is large due to the abnormal behavior of the user, the main cause of the increase in the fluctuation information is considered to be the user.

  In addition, since abnormal behavior by the user is rarely continued continuously, it is possible that the state is “state A” or “state B” at the time of inspection by the maintenance worker. For this reason, it is important to determine what trajectory has passed in FIG. 4 before reaching the “abnormal state B” which is a true abnormal state.

  A method for determining this will be described. In the case of "state B", it is important how much "state D" or "state C" has been passed so far. The number of times "state D" was Nd and "state C" It is assumed that the number of times is Nc, the time that has reached “State D” is ti, and the time that has reached “State C” is tj with respect to the time t that has reached “State B”. The output of the behavior information in “state D” is yd (ti), and the output of the fluctuation information in “state C” is yc (tj). At this time,

Is calculated. Here, R (t) is an operation for determining a section of an area as shown in FIG. 4, for example, and determines the correlation state.

  Note that f (t) is a function that monotonically decreases as t increases.

, Or a function that becomes “0” when t is equal to or more than a certain value. Based on the magnitude of R (t) calculated in this way, it is possible to determine whether or not there is a high possibility of an abnormal state based on artificial behavior.

  Although FIG. 4 shows four groups of “state A” to “state D”, the number of groups for making a determination is not limited to four groups, and may be appropriately set according to each elevator. is there.

  Further, the function shown in equation (1) may be any function that can evaluate what state has been reached before reaching "state B". For example, "function D" or "state C" It is also possible to use the elapsed time from when the condition has been reached or the number of times that the state has reached “State D” or “State C” before reaching “State B”.

  Next, a second embodiment of the present invention will be described with reference to FIG. The second embodiment is different from the second embodiment in that an acceleration sensor 15 is used in place of the load sensor 3 in order to detect an abnormal state caused by artificial abnormal behavior by a user.

  In FIG. 5, the entire configuration is the same as that of the first embodiment, but an acceleration sensor 15 is provided at the bottom of the car. Therefore, when acceleration occurs in the car due to the user jumping in the car, the behavior of the user can be detected by the acceleration sensor 15. The output information of the acceleration sensor 15 is sent to the abnormal operation detecting means 7, and the abnormal operation detecting means 7 can determine whether the abnormal state is caused by the abnormal behavior of the user.

  If the acceleration sensor 15 is attached to the door of the car, it is possible to detect that the user has collided with the door. As a result, the accuracy of directly detecting whether the user has performed an abnormal behavior can be increased. Generally, the user rarely intentionally gives an impact to the door, and when an impact on the door is detected by the acceleration sensor 15 provided on the door, it is determined that an abnormal state has occurred due to the abnormal behavior of the user. can do.

  Further, in addition to this, when user behavior information can be acquired from a portable sensor device owned by the user, the behavior of the user can be detected using the behavior information. Further, if the behavior of the user can be detected, the acceleration sensor 15 need not be used, and for example, a signal from a position sensor provided on the hoistway may be used.

  In the above-described embodiment, the abnormal state of the element device is described using the fluctuation information due to the aging as the fluctuation information of the element device. However, other causes, such as physical and electrical It goes without saying that an abnormal state of the element device may be determined using the fluctuation information of the element device caused by the action.

  As described above, according to the present invention, the output information of the behavior sensor that acquires the information on the behavior of the user of the elevator to be diagnosed (the system to be diagnosed) to be diagnosed, and the elevator to be diagnosed (the system to be diagnosed) The behavior of the user of the elevator to be diagnosed (the system device to be diagnosed) is compared with the output information of the behavior sensor of the first comparison elevator (the first system device for comparison), which is the same as the behavior sensor of the same and similar in business type and application. Information, and output information of an element device sensor for obtaining information on the element devices constituting the elevator to be diagnosed (system device to be diagnosed); Elements of the second comparative elevator (second comparative system device) of the same model as the diagnostic elevator (diagnosed system device) By comparing the output information of the device sensor with the information on the operation of the component devices of the elevator to be diagnosed (diagnosed system device), based on the correlation between the behavior information of the user and the variation information of the component devices, the elevator to be diagnosed ( It is configured to judge whether an abnormal state has occurred in the system to be diagnosed) due to an artificial cause.

  According to this, the abnormal state generated in the elevator to be diagnosed (diagnosed system equipment) is an abnormal state caused by an artificial cause, or the cause based on the element equipment of the elevator to be diagnosed (diagnosed system equipment). It is possible to identify at an early stage whether or not an abnormal state has occurred, and it is possible to shorten the suspension period of the elevator to be diagnosed (diagnosed system device) and improve the convenience for the user.

  Note that the present invention is not limited to the above-described embodiment, and includes various modifications. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described above. Further, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of one embodiment can be added to the configuration of another embodiment. Also, for a part of the configuration of each embodiment, it is possible to add, delete, or replace another configuration.

  DESCRIPTION OF SYMBOLS 1 ... Human sensor, 2 ... Surveillance camera, 3 ... Load sensor, 4 ... Door sensor, 5 ... Sensor A, 6 ... Sensor B, 7 ... Abnormal operation detection means, 8 ... Sensor group of similar use elevator, 9 ... Normal Time variation information storage means, 10: variation information detection means, 11: elevator sensor group of the same model, 12: aging information storage means, 13: cause determination means, 14: output device, 15: acceleration sensor.

Claims (8)

The output information of the behavior sensor that acquires information on the behavior of the user of the system device to be diagnosed, and the first comparison system device having the same business type and application as the behavior sensor of the system device to be diagnosed. Behavior detection means for comparing the output information of the behavior sensor to determine behavior information of the user of the system under diagnosis,
Output information of an element device sensor for acquiring information on element devices constituting the system device to be diagnosed, and a second comparison system of the same model as the system device to be diagnosed, which is the same as the element device sensor of the system device to be diagnosed Fluctuation information detecting means for comparing with the output information of the component device sensor of the device to determine the variation information on the operation of the component device of the system under diagnosis,
From the correlation between the behavior information of the user obtained by the behavior detection means and the fluctuation information of the element devices obtained by the fluctuation information detection means, whether an abnormal state has occurred in the system to be diagnosed due to an artificial cause. An abnormality diagnosis device for a system device, comprising: cause determination means for determining whether or not the abnormality has occurred. The output information of the behavior sensor that acquires the information on the behavior of the user of the elevator to be diagnosed to be diagnosed, and the behavior sensor of the first comparative elevator having the same business type and application as the behavior sensor of the elevator to be diagnosed. Behavior detecting means for comparing the output information to obtain behavior information of the user of the diagnosed elevator;
Output information of an element device sensor for acquiring information on element devices constituting the elevator to be diagnosed, and element devices of a second comparison elevator of the same model as the elevator to be diagnosed, the same as the element device sensor of the elevator to be diagnosed Fluctuation information detection means for comparing with output information of a sensor to obtain fluctuation information relating to the operation of the element device of the elevator to be diagnosed,
From the correlation between the behavior information of the user obtained by the behavior detection means and the fluctuation information of the element devices obtained by the fluctuation information detection means, whether or not an abnormal state has occurred in the elevator to be diagnosed due to an artificial cause. An abnormality diagnosis device for an elevator, comprising: cause determination means for determining whether the abnormality has occurred. The abnormality diagnosis device for an elevator according to claim 2,
The output information of the behavior sensor of the first comparison elevator is output information having a width of a first predetermined range, and the behavior detection unit determines that the output information of the behavior sensor of the elevator to be diagnosed is the first information. If it exceeds the predetermined range, it is determined that there is abnormal behavior of the user,
The output information of the elementary device sensor of the second comparison elevator is output information having a width of a second predetermined range, and the fluctuation information detection unit outputs the output information of the elementary device sensor of the elevator under diagnosis. An abnormality diagnosis device for an elevator, wherein it is determined that an abnormality is present in the element device if the value exceeds a second predetermined range. The abnormality diagnosis device for an elevator according to claim 3,
The cause determination unit is configured to determine the cause of the artificial elevator due to the correlation between the magnitude of the behavior information obtained by the behavior detection unit and the magnitude of the variation information obtained by the variation information detection unit. An abnormality diagnosis device for an elevator, which determines whether or not an abnormal state has occurred in the elevator. The abnormality diagnosis device for an elevator according to claim 3 or 4,
When the cause determination unit detects the abnormality of the element device within a predetermined time after detecting the abnormal behavior of the user, the abnormality of the element device generated at this time is caused by a cause of the user. An abnormality diagnosis apparatus for an elevator, wherein an abnormality is determined, and when an abnormality of the element device is detected after the predetermined time has passed, it is determined that the element device is a true abnormality. The abnormality diagnosis device for an elevator according to claim 3 or 4,
The behavior sensor is a load sensor that detects the load of the user present in the car or a sensor camera that monitors the behavior of the user, and the element device sensor is a door sensor that detects the opening / closing speed of the car door. An abnormality diagnosis device for an elevator, characterized in that: The abnormality diagnosis device for an elevator according to claim 3 or 4,
The fluctuation information detecting means is configured to detect the element of the elevator to be diagnosed based on aging degradation information indicating aging of the element device detected by the element device sensor of the elevator to be diagnosed and the second comparison elevator. An abnormality diagnosis apparatus for an elevator, wherein a degree of temporal deterioration of equipment is obtained. The output information of the behavior sensor that acquires the information on the behavior of the user of the elevator to be diagnosed to be diagnosed, and the behavior sensor of the first comparative elevator having the same business type and application as the behavior sensor of the elevator to be diagnosed. Determine the behavior information of the user of the elevator under diagnosis by comparing the output information,
Output information of an element device sensor for acquiring information on element devices constituting the elevator to be diagnosed, and element devices of a second comparison elevator of the same model as the elevator to be diagnosed, the same as the element device sensor of the elevator to be diagnosed Compared with the output information of the sensor to determine the variation information on the operation of the element device of the elevator to be diagnosed,
Said behavior information of the user, from the correlation of the fluctuation information regarding the operation of the element devices, elevators, characterized in that determining whether the abnormal state artificial cause the diagnostic elevator caused abnormal Diagnostic method.

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