Patent Description:
The present disclosure relates to a technology for diagnosing an abnormality in a battery, and more particularly, to a battery diagnosing apparatus, a battery system, and a battery diagnosing method capable of identifying an abnormal pattern that causes abnormal voltage behavior of the battery.

As applications requiring high voltage become widespread, a battery system employing a structure in which a plurality of batteries are connected in series is widely used. As the number of batteries included in the battery system increases, the frequency of battery abnormalities inevitably increases. Accordingly, the need for a diagnostic technology to accurately detect a battery abnormality is increasing.

Recently, the method of detecting abnormality of a battery based on battery information including a plurality of parameters related to the battery (e.g., voltage, current, temperature, etc. of the battery) and the usage state of the battery (e.g., charging, discharging, resting) is widely used.

However, the above detection method has a disadvantage in that a large amount of computation and a long time are required to detect abnormalities of each battery because the process for a battery diagnosing apparatus to monitor battery information using various sensors is essential.

Meanwhile, there is an attempt to detect abnormal voltage behavior of each battery by comparing a plurality of battery voltages obtained from a plurality of batteries at a specific time point or a predetermined time unit with each other while excluding the parameters other than voltage. However, the voltage-based abnormal detection may only detect whether each battery exhibits abnormal voltage behavior, and does not provide information on why the abnormal voltage behavior appears.

<CIT> relates to a method for diagnosing the operating state of an electrochemical system in real-time comprising a stack of cells, said method comprising steps for performing voltage measurements of said cells. Said method further comprises: real-time processing of the voltage measurements thus performed, in order to extract specific waveforms therefrom, converting said specific waveforms in order to generate specific points therefrom of the real-time operation of the electrochemical system, and comparing these specific points of the real-time operation and specific points of an off-line operation of the electrochemical system that originate from a conversion of specific waveforms extracted from voltage measurements performed off-line, while the electrochemical system is placed in known operating states including fault states, so as to produce information relating to the real-time operating state of the electrochemical system.

The present disclosure is designed to solve the problems of the related art, and therefore the present disclosure is directed to providing a battery diagnosing apparatus, a battery system and a battery diagnosing method, which may identify an abnormal pattern causing abnormal voltage behavior of each battery by comparing input time series of each battery extracted as indicating abnormal voltage behavior through voltage-based abnormality detection with a plurality of reference time series that are one-to-one associated with a plurality of different abnormal patterns, without monitoring parameter(s) other than the battery voltage.

A battery diagnosing apparatus according to one aspect of the present disclosure is as provided according to claim <NUM>.

The control unit may be configured to identify an abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with a minimum matching index among the plurality of matching indexes.

In a further aspect, there is provided an apparatus according to claim <NUM>.

The control unit may be configured to add the abnormal input time series to the database as a new reference time series corresponding to the identified abnormal pattern.

A battery system according to another aspect of the present disclosure comprises the battery diagnosing apparatus of claim <NUM>.

A battery diagnosing method according to still another aspect of the present disclosure is as provided according to claim <NUM>. Further aspects are provided according to claims <NUM> and <NUM>.

According to at least one of the embodiments of the present disclosure, it is possible to identify an abnormal pattern causing an abnormal voltage behavior of each battery by comparing the input time series of each battery extracted as indicating abnormal voltage behavior through voltage-based abnormality detection with the plurality of reference time series one-to-one associated to a plurality of different abnormal patterns, without monitoring parameter(s) other than the battery voltage.

In addition, according to at least one of the embodiments of the present disclosure, for a pair of an input time series and each reference time series, it is possible to determine a matching index between the input time series and each reference time series by combining two similar values determined through dynamic time warping and Pearson correlation coefficient. Then, it is possible to identify an abnormal pattern of the battery exhibiting abnormal voltage behavior from the one-to-one corresponding relationship between the plurality of reference time series, the plurality of matching indexes and the plurality of abnormal patterns.

Hereinafter, a preferred embodiment of the present disclosure will be described in detail with reference to the accompanied drawings.

In addition, a term such as <control unit> used in the specification means a unit that processes at least one function or operation, and may be implemented as hardware, software, or a combination of hardware and software.

<FIG> is a diagram exemplarily showing the configuration of a battery system according to the present disclosure.

Referring to <FIG>, the battery system <NUM> may be a power device including an electric load driven using a discharging power of a battery, such as an electric vehicle. Alternatively, the battery system <NUM> may be a charging and discharging test device used to manufacture a battery and provided to test the performance of a battery in the manufacturing processes.

The battery system <NUM> includes a battery assembly <NUM>, a switch <NUM>, a charging and discharging circuit <NUM>, an upper controller <NUM> and a battery diagnosing apparatus <NUM>. The upper controller <NUM> is in charge of the overall charging and discharging procedure of the battery assembly <NUM>. That is, the upper controller <NUM> controls the charging and discharging circuit <NUM> directly or indirectly through the battery diagnosing apparatus <NUM> according to a predetermined charging and discharging schedule.

The battery assembly <NUM> includes a plurality of batteries B<NUM> to Bn (n is a natural number equal to or greater than <NUM>).

The plurality of batteries B<NUM> to Bn may be electrically connected to each other in series as a single group. Alternatively, the plurality of batteries B<NUM> to Bn may be grouped into two or more to enable charging and discharging independently of each other. Hereinafter, in describing the common contents of the plurality of batteries B<NUM> to Bn, the reference symbol 'B' will be used to refer to the battery. The type of the battery B is not particularly limited as long as it can be repeatedly charged and discharged, such as a lithium-ion battery.

The switch <NUM> is installed on a power line PL that connects power input/output terminals of the battery assembly <NUM> and power input/output terminals of the charging and discharging circuit <NUM>. While the switch <NUM> is turned on, power may be transferred from one of the battery assembly <NUM> and the charging and discharging circuit <NUM> to the other. The switch <NUM> may be implemented by using any one of known switching devices such as a relay, a FET (Field Effect Transistor), and the like, or by combining two or more thereof. The control unit <NUM> may turn on/off the switch <NUM> according to the state of the battery assembly <NUM>.

The charging and discharging circuit <NUM> is operatively coupled to the battery diagnosing apparatus <NUM> via the upper controller <NUM>. When two components are operatively coupled, it means that the two components are connected to transmit and receive signals in one direction or in both directions. The charging and discharging circuit <NUM> may generate a DC power for charging each group of the battery assembly <NUM> from an AC power supplied by an external power source. The charging and discharging circuit <NUM> may convert a DC power from each group of the battery assembly <NUM> into an AC power and/or a DC power having a different voltage level, and transmit the converted power to an electrical load (not shown).

The battery diagnosing apparatus <NUM> includes a voltage measuring circuit <NUM>, a database <NUM>, and a control unit <NUM> that are operatively coupled to each other. The battery diagnosing apparatus <NUM> may further include an interface unit <NUM>.

The voltage measuring circuit <NUM> is provided to be electrically connectable to a positive electrode terminal and a negative electrode terminal of each battery B. The voltage measuring circuit <NUM> is configured to measure a battery voltage that is a voltage across both ends of each battery B, and generate a voltage signal representing the measured battery voltage.

The database <NUM> records a plurality of abnormal patterns, a plurality of reference time series and a predetermined corresponding relationship between the plurality of abnormal patterns and the plurality of reference time series. The plurality of reference time series are associated one-to-one or many-to-one with a plurality of different abnormal patterns.

The plurality of abnormal patterns cause abnormal voltage behavior of the battery B and may depend on the internal structure, manufacturing method, electrode material, etc. of the battery B. For example, metal lithium deposition on the surface of the negative electrode, partial tearing of the positive electrode tab, disconnection of the positive electrode tab and/or the negative electrode tab, bending of the negative electrode tab, short circuit between the positive electrode plate and the negative electrode plate through separator, or the like may be set to abnormal patterns, respectively.

Each reference time series is a data set of temporal changes in battery voltage obtained in advance from a battery having a corresponding abnormal pattern.

In the database <NUM>, a program and various data necessary for executing a battery diagnosing method according to embodiments to be described later may be stored in advance. The database <NUM> may include at least one type of storage medium, for example, among a flash memory type, a hard disk type, an SSD (Solid State Disk) type, an SDD (Silicon Disk Drive) type, a multimedia card micro type, a RAM (Random Access Memory), a SRAM (Static Random access Memory), a ROM (Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), and a PROM (Programmable Read-Only Memory).

The control unit <NUM>, in hardware, may be implemented using at least one of ASICs (Application Specific Integrated Circuits), DSPs (Digital Signal Processors), DSPDs (Digital Signal Processing Devices), PLDs (Programmable Logic Devices), FPGAs (Field Programmable Gate Arrays), microprocessors, and electrical units for performing other functions.

The control unit <NUM> may be additionally operatively coupled to the switch <NUM> and/or the interface unit <NUM>.

The interface unit <NUM> may be communicatively coupled to the upper controller <NUM> of the battery system <NUM>. The interface unit <NUM> may transmit a message from the upper controller <NUM> to the control unit <NUM> and may transmit a message from the control unit <NUM> to the upper controller <NUM>. The message from the control unit <NUM> may include information for notifying an abnormality of each battery B. For communication between the interface unit <NUM> and the upper controller <NUM>, for example, a wired network such as a LAN (Local Area Network), a CAN (Controller Area Network), a daisy chain, and/or a near-distance wireless network such as Bluetooth, Zigbee, Wi-Fi, etc. may be utilized. The interface unit <NUM> may include an output device (e.g., a display, a speaker) that provides information received from the control unit <NUM> and/or the upper controller <NUM> in a user-recognizable form. The upper controller <NUM> may control the charging and discharging circuit <NUM> based on the battery information (e.g., abnormal voltage behavior) collected through communication with the battery diagnosing apparatus <NUM>.

The control unit <NUM> may execute a diagnostic mode for detecting abnormalities of the plurality of batteries B<NUM> to Bn at all times during operation or according to a request from the upper controller <NUM>.

Now, with reference to <FIG>, the operation of the control unit <NUM> during the execution of the diagnostic mode will be described.

<FIG> is a diagram exemplarily showing the results of a battery voltage of a normal battery and a battery voltage of an abnormal battery obtained over a time region having the same use condition.

The control unit <NUM> collects the voltage signal from the voltage measuring circuit <NUM> at every unit time. The unit time is predetermined, and may be, for example, an integer multiple of a time interval at which a voltage signal is generated by the voltage measuring circuit <NUM>.

The control unit <NUM> generates a plurality of input time series associated with the plurality of batteries B<NUM> to Bn based on the voltage signal collected from the voltage measuring circuit <NUM>. The generation of the plurality of input time series may be repeated at every predetermined unit time.

The control unit <NUM> may generate a plurality of input time series based on the voltage signals collected over a time region in which the plurality of batteries B<NUM> to Bn are charged, discharged, or idle under the same use condition (e.g., a charging current, a charging voltage, a discharging current, a discharging voltage, a temperature, etc.).

When the plurality of batteries B<NUM> to Bn are serially connected to each other to form a single group, all of the plurality of batteries B<NUM> to Bn may be treated as having the same use condition.

When the plurality of batteries B<NUM> to Bn are divided into a plurality of groups, a plurality of time regions in which a plurality of groups have the same use conditions may be identified based on the history of the use condition of each of the plurality of groups, and a plurality of input time series may be generated based on the voltage signal collected over each identified time region. As an example, when the charging and discharging circuit <NUM> has conducted a charging and discharging event for the first group with a specific use condition over the first time region from <NUM>:<NUM> to <NUM>:<NUM> of a specific day and the charging and discharging circuit <NUM> has conducted a charging and discharging event for the second group with the same use condition over the second time region from <NUM>:<NUM> to <NUM>:<NUM> of the same specific day, the control unit <NUM> may generate an input time series associated with the first time region for each battery B of the first group and generate an input time series associated with the second time region for each battery B of the second group. The control unit <NUM> may collect the input time series obtained from each battery B of the first group over the first time region and the input time series obtained from each battery B of the second group over the second time region as the same control group.

A moving window may be used to generate the input time series. The moving window has a predetermined time size, and a starting point may be a time point preceding a specific time point by the predetermined time size, and an ending point may be the specific time point. The signal length of the input time series corresponds to the time size of the moving window. For example, when the unit time is <NUM> seconds and the moving window is <NUM> seconds, the signal length of the input time series may be <NUM> seconds/<NUM> seconds = <NUM>. That is, the input time series may be a vector in which <NUM> voltage values representing the change in sequentially measured battery voltages are arranged on the time axis. At every unit time, each of the starting and ending points of the moving window may be increased by the unit time. Each input time series is a data set representing the temporal change of the battery voltage of the battery B associated therewith.

In <FIG>, the horizontal axis is time, the vertical axis is battery voltage, and t<NUM> and tA are the starting and ending points of the time region extracted as having the same use condition, respectively. The curve <NUM> corresponds to the input time series of the normal battery, and the curve <NUM> corresponds to the input time series of the abnormal battery. In general, among the plurality of batteries B<NUM> to Bn, the number of normal batteries B will be much greater than the number of abnormal batteries B. Taking this into consideration, the control unit <NUM> extracts an abnormal input time series <NUM> from the plurality of input time series by comparing the plurality of input time series with each other. The abnormal input time series <NUM> refers to each input time series exhibiting an abnormal voltage behavior that exceeds a certain level from the overall voltage behavior (e.g., average) of the plurality of input time series. The abnormal voltage behavior may be judged using at least one of various methods, such as whether the input time series of each battery has a voltage drop (or voltage rise) that exceeds a predetermined value at a specific time point or for a predetermined time, whether the input time series of each battery has a difference exceeding a predetermined value from an average (or median value) of the plurality of input time series, and the like, and a detailed description thereof will be omitted.

<FIG> and <FIG> are exemplary graphs referenced for explaining a comparison procedure between an abnormal input time series and a plurality of reference time series. In <FIG>, the horizontal axis represents time, and the vertical axis represents battery voltage.

The control unit <NUM> may identify an abnormal pattern of the abnormal input time series among a plurality of abnormal patterns given in advance by comparing the abnormal input time series <NUM> with the plurality of reference time series one by one. For example, the control unit <NUM> may compare the plurality of reference time series with the abnormal input time series <NUM> one by one in order according to an order given in advance for the plurality of reference time series. As another example, the control unit <NUM> may compare two or more of the plurality of reference time series with the abnormal input time series <NUM> at the same time, and compare the remaining reference time series with the abnormal input time series <NUM> in the same way. Identification of the abnormal pattern means to judge which fault state the battery B associated with the abnormal input time series has.

In <FIG>, the curve <NUM> is the same as the curve <NUM> of <FIG>, and the curve <NUM> corresponds to any one reference time series among the plurality of reference time series. In <FIG>, tR is an ending point of the reference time series <NUM> when the starting points of the abnormal input time series <NUM> and the reference time series <NUM> coincide with each other. That is, <FIG> illustrates that the abnormal input time series <NUM> is shorter than the reference time series <NUM>.

The control unit <NUM> may calculate a similarity value DA1 representing a signal distance between the abnormal input time series <NUM> and the reference time series <NUM> by using dynamic time warping. As the abnormal input time series <NUM> is similar to the reference time series <NUM>, the similarity value DA1 approaches <NUM>, and if the abnormal input time series <NUM> is completely identical to the reference time series <NUM>, the similarity value DA1 is <NUM>. The control unit <NUM> may convert the abnormal input time series <NUM> and the reference time series <NUM> into an arranged time series <NUM> and an arranged time series <NUM> having the same signal length, respectively, by using dynamic time warping. That is, by dynamic time warping, the time axis of the shorter one <NUM> may be extended so that the shorter one of the abnormal input time series <NUM> and the reference time series <NUM> matches the signal length of the longer one <NUM>. As described above, <FIG> illustrates that the reference time series <NUM> is longer than the abnormal input time series <NUM>, and the arranged time series <NUM> is illustrated as being the same as the reference time series <NUM>. Since the signal distance between two signals by dynamic time warping and the alignment of the time axis of the two signals are well known, a detailed description thereof will be omitted.

The control unit <NUM> may calculate a similarity value DB1 representing a Pearson correlation coefficient between the arranged time series <NUM> and the arranged time series <NUM>. As the arranged time series <NUM> is similar to the arranged time series <NUM>, the similarity value DB1 approaches <NUM>, and if the arranged time series <NUM> is completely identical to the arranged time series <NUM>, the similarity value DB1 is <NUM>.

Referring to <FIG>, the curve <NUM> exemplifies a normalized time series that is the result of maximum-minimum normalization for the curve <NUM> of <FIG>, and the curve <NUM> exemplifies a normalized time series that is the result of maximum-minimum normalization of the curves <NUM>, <NUM> of <FIG>. In <FIG>, the horizontal axis is time, and the vertical axis represents a normalized battery voltage ranging from <NUM> to <NUM>. The control unit <NUM> may convert the abnormal input time series <NUM> and the reference time series <NUM> into a normalized time series <NUM> and a normalized time series <NUM>, respectively, by using maximum-minimum normalization.

The control unit <NUM> may calculate a similarity value DA2 representing a signal distance between the normalized time series <NUM> and the normalized time series <NUM> by using dynamic time warping. The control unit <NUM> may convert the normalized time series <NUM> and the normalized time series <NUM> into an arranged time series <NUM> and an arranged time series <NUM> having the same time length, respectively, by using dynamic time warping. That is, by dynamic time warping, the shorter time axis <NUM> of the normalized time series <NUM> and the normalized time series <NUM> may be extended so that the shorter one <NUM> matches the signal length of the longer one <NUM>. As described above, <FIG> illustrates that the reference time series <NUM> is longer than the abnormal input time series <NUM>, and the arranged time series <NUM> is illustrated in <FIG> as being identical to the normalized time series <NUM>. Alternatively, the control unit <NUM> may obtain the arranged time series <NUM> by normalizing the curve <NUM> of <FIG> by using maximum-minimum normalization.

The control unit <NUM> may calculate a similarity value DB2 representing a Pearson correlation coefficient between the arranged time series <NUM> and the arranged time series <NUM>. As the arranged time series <NUM> is similar to the arranged time series <NUM>, the similarity value DB2 approaches <NUM>, and if the arranged time series <NUM> is completely identical to the arranged time series <NUM>, the similarity value DB2 is <NUM>.

So far, it has been explained that the control unit <NUM> may calculate two similarity values DA1, DA2 representing the signal distance and two similarity values DB1, DB2 representing the Pearson correlation coefficient as a result of comparison between the abnormal input time series <NUM> and the reference time series <NUM>. Of course, the control unit <NUM> may calculate both of the two similarity values DA1, DA2 representing the signal distance, or calculate only one of the two similarity values DA1, DA2 and omit the calculation of the other. Similarly, the control unit <NUM> may calculate both of the two similarity values DB1, DB2 representing the Pearson correlation coefficient, or calculate only one of the two similarity values DB1, DB2 and omit the calculation of the other.

The control unit <NUM> may determine a matching index between the abnormal input time series <NUM> and the reference time series <NUM> based on at least one of the two similarity values DA1, DA2 and at least one of the two similarity values DB1, DB2. The matching index may be obtained from a function given in advance so that its value is reduced as the abnormal input time series <NUM> is similar the reference time series <NUM>. For example, the control unit <NUM> may determine the matching index to be identical to a value obtained by dividing the similarity value DA1 by the similarity value DB1.

As another example, the control unit <NUM> may determine the matching index to be identical to a value obtained by dividing the similarity value DA1 by the similarity value DB2.

As still another example, the control unit <NUM> may determine the matching index to be identical to a value obtained by dividing the similarity value DA2 by the similarity value DB1.

As still another example, the control unit <NUM> may determine the matching index to be identical to a value obtained by dividing the similarity value DA2 by the similarity value DB2.

As still another example, the control unit <NUM> may determine the matching index to be identical to a value obtained by dividing the product of the similarity value DA1 and the similarity value DA2 by the similarity value DB1.

As still another example, the control unit <NUM> may determine the matching index to be identical to a value obtained by dividing the product of the similarity value DA1 and the similarity value DA2 by the similarity value DB2.

As still another example, the control unit <NUM> may determine the matching index to be identical to a value obtained by dividing the similarity value DA1 by the product of the similarity value DB1 and the similarity value DB2.

As still another example, the control unit <NUM> may determine the matching index to be identical to a value obtained by dividing the similarity value DA2 by the product of the similarity value DB1 and the similarity value DB2.

As still another example, the control unit <NUM> may determine the matching index to be identical to a value obtained by dividing the product of the similarity value DA1 and the similarity value DA2 by the product of the similarity value DB1 and the similarity value DB2.

In this regard, even with the same abnormal pattern, the battery voltage may be affected by other parameter(s) of the battery B (e.g., current, temperature, SOC (State Of Charge) and SOH (State Of Health), etc.). The maximum-minimum normalization may partially reduce the dissimilarity between the abnormal input time series <NUM> and the reference time series <NUM> caused by different parameter(s). Therefore, in determining the matching index associated with each reference time series, when at least one of the similarity values DA1, DB1 is combined with at least one of the similarity values DA2, DB2, more accurate abnormal patterns may be identified.

The control unit <NUM> may determine a plurality of matching indexes one-to-one corresponding to the plurality of reference time series by comparing the plurality of reference time series with the abnormal input time series <NUM> one by one. Then, the control unit <NUM> may determine a minimum matching index among the plurality of matching indexes, obtain an abnormal pattern corresponding to the reference time series of the minimum matching index among the plurality of abnormal patterns from the database <NUM>, and identify the abnormal pattern of the abnormal input time series <NUM> to be identical to the obtained abnormal pattern.

<FIG> is a flowchart exemplarily showing a battery diagnosing method according to the present disclosure. The method of <FIG> may be performed by the battery diagnosing apparatus <NUM> shown in <FIG>.

Referring to <FIG>, in Step S510, the control unit <NUM> collects a voltage signal representing a battery voltage that is a voltage across both ends of each of the plurality of batteries B<NUM> to Bn from the voltage measuring circuit <NUM>.

In Step S520, the control unit <NUM> generates a plurality of input time series representing a change history of the battery voltage of each of the plurality of batteries B<NUM> to Bn over a time region in which the plurality of batteries B<NUM> to Bn have the same use condition, based on the voltage signal.

In Step S530, the control unit <NUM> extracts an abnormal input time series <NUM> representing abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other.

In Step S540, the control unit <NUM> identifies an abnormal pattern of the abnormal input time series <NUM> among a plurality of abnormal patterns having a predetermined corresponding relationship with the plurality of reference time series by comparing the abnormal input time series <NUM> with the plurality of reference time series one by one. The control unit <NUM> calculates at least one of the two similarity values DA1, DA2 and at least one of the two similarity values DB1, DB2 for each of the plurality of reference time series, and combines the calculated values to obtain a plurality of matching indexes one-to-one corresponding to the plurality of reference time series. Then, the control unit <NUM> identifies the abnormal pattern of the abnormal input time series <NUM> to be identical to the abnormal pattern corresponding to the reference time series associated with the minimum matching index among the plurality of matching indexes.

In step S550, the control unit <NUM> may add the abnormal input time series <NUM> to the database <NUM> as a new reference time series corresponding to the abnormal pattern identified in Step S540. Accordingly, the identification performance of the abnormal pattern using the plurality of reference time series may be enhanced. Additionally, the control unit <NUM> may transmit the abnormal pattern of the abnormal input time series <NUM> to the upper controller <NUM>, or output visual and/or auditory feedback for notifying the abnormal pattern of the abnormal input time series <NUM> to the user through the interface unit <NUM>.

Claim 1:
A battery diagnosing apparatus (<NUM>), comprising:
a voltage measuring circuit (<NUM>) configured to generate a voltage signal representing a battery voltage that is a voltage across both ends of each of a plurality of batteries (<NUM>);
a database (<NUM>) in which a plurality of abnormal patterns, a plurality of reference time series, and a predetermined corresponding relationship between the plurality of abnormal patterns and the plurality of reference time series are recorded; and
a control unit (<NUM>) configured to generate a plurality of input time series representing a change history of the battery voltage of each of the plurality of batteries (<NUM>), based on the voltage signal,
wherein the control unit (<NUM>) is configured to extract an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other, and
characterised in that:
for each of the plurality of reference time series, the control unit (<NUM>) is configured to:
calculate a first similarity value representing a signal distance between the abnormal input time series and each reference time series by using dynamic time warping,
convert the abnormal input time series and each reference time series into a first arranged time series and a second arranged time series having the same time length, respectively, by using the dynamic time warping,
calculate a second similarity value representing a Pearson correlation coefficient between the first arranged time series and the second arranged time series, and
determine a matching index between the abnormal input time series and each reference time series by combining the first similarity value and the second similarity value,
wherein the control unit (<NUM>) is configured to identify an abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with one of the plurality of matching indexes determined for the plurality of reference time series.