WO2019016892A1 - 品質分析装置及び品質分析方法 - Google Patents
品質分析装置及び品質分析方法 Download PDFInfo
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
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- G05B19/02—Programme-control systems electric
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- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
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Definitions
- the present invention relates to a quality analysis apparatus and a quality analysis method that enable estimation of a cause of a trend change in a manufacturing process or a test process of a product and a generation cause of a product failure.
- the causes of defects (variation in quality, deterioration in yield, deterioration in tact time, increase in defective products, breakdown of equipment, etc.) at the manufacturing site are often identified by the knowledge and experience of the manufacturing site.
- candidate causes of defects are extracted based on the knowledge and experience of the manufacturing site, there is a problem that it is not clear whether candidate causes of the defects are causes of true defects.
- extracting causes of failure use information such as product manufacturing conditions, test conditions, and test results obtained from sensors provided in manufacturing equipment and testing equipment at manufacturing sites as quality data representing the product status Is valid.
- the apparatus at the manufacturing site is composed of a large number of sensors, and in each sensor, values corresponding to the sensors, such as current and temperature, are acquired as quality data.
- the type of quality data corresponding to the sensor such as current or temperature, is called a data item.
- the feature of the defect is obtained by comparing the frequency distribution of the defective quality data with the frequency distribution of the quality data without the defect.
- occurrence of a defect is a premise, and it is difficult to extract gradually changing quality data although it does not lead to a defect.
- the extraction of the failure factor by comparing the frequency distribution depends on the analyst who checks the frequency distribution, so the load of the confirmation increases as the number of data items increases.
- estimation of the cause of the difference is often judged qualitatively by the experience of the worker, and it is difficult to identify the cause of the failure. .
- the present invention was made in order to solve such problems, and aims to provide a quality analysis device and a quality analysis method capable of speeding up the extraction of failure factor candidates and easily inferring the occurrence of a failure. I assume.
- the quality analysis apparatus includes: a data aggregation unit that acquires quality data indicating a state of a quality analysis object; and apparatus information data indicating information of an apparatus that handles the quality analysis object; A condition setting unit that sets, for quality data and device information data, a data item to be aggregated, a reference condition indicating a condition serving as a basis for quality analysis, and a comparison condition indicating a condition to be quality analysis From the quality data acquired by the data aggregation unit and the device information data, the data satisfying the reference condition of the data item set by the condition setting unit and the data satisfying the comparison condition are extracted, and the frequency distribution is determined for each data item.
- a distribution difference calculation unit which calculates and outputs data indicating the degree of deviation of the frequency distribution of the reference condition and the comparison condition.
- the quality analysis apparatus sets data items to be subjected to quality analysis, reference conditions, and comparison conditions, and outputs data indicating the degree of deviation between the reference conditions and the comparison conditions for each data item. It is. As a result, it is possible to quickly extract the candidate for the cause of the failure and to easily estimate the occurrence of the failure.
- FIG. 1 is a block diagram of a quality analysis apparatus according to the present embodiment.
- the quality analysis apparatus according to the present embodiment includes a data aggregation unit 1, a data type classification unit 2, a condition setting unit 3, and a distribution difference calculation unit 4.
- the data aggregation unit 1 is a processing unit that acquires quality data and device information data.
- the data type classification unit 2 is a processing unit that classifies the quality data and the device information data acquired by the data aggregation unit 1 according to a set predetermined rule.
- the condition setting unit 3 For the data acquired by the data aggregation unit 1 or the data classified by the data type classification unit 2, the condition setting unit 3 includes data items to be aggregated and a reference condition indicating a condition serving as a basis for quality analysis.
- the processing unit sets a comparison condition indicating a condition to be a quality analysis target.
- the distribution difference calculation unit 4 extracts data satisfying the condition set by the condition setting unit 3 from the data acquired by the data aggregation unit 1 or the data classified by the data type classification unit 2, for each data item. It is a processing unit that calculates the frequency distribution and outputs data indicating the degree of divergence of the data of the reference condition and the comparison condition.
- FIG. 2 is a hardware configuration diagram of the quality analysis device of the first embodiment.
- the illustrated quality analysis apparatus is configured using a computer, and includes a processor 101, an auxiliary storage device 102, a memory 103, an input interface (input I / F) 104, a display interface (display I / F) 105, an input device 106, and a display 107. , Signal line 108, and cables 109, 110.
- the processor 101 is connected to other hardware via a signal line 108.
- the input I / F 104 is connected to the input device 106 via the cable 109.
- the display I / F 105 is connected to the display 107 via a cable 110.
- each functional unit in the quality analysis device is realized by software, firmware, or a combination of software and firmware.
- the software or firmware is described as a program and stored in the auxiliary storage device 102.
- This program causes a computer to execute the procedure or method of each functional unit.
- the processor 101 implements the functions of the data aggregation unit 1 to the distribution difference calculation unit 4 in FIG. 1 by reading and executing the program stored in the auxiliary storage device 102. Further, time-series data is also stored in the auxiliary storage device 102. Furthermore, output data such as frequency distribution may be stored in the auxiliary storage device 102.
- the program and the quality data and the device information data stored in the auxiliary storage device 102 are loaded into the memory 103, and the processor 101 reads them to realize each function and execute each processing.
- the execution result is written to the memory 103 and stored as output data in the auxiliary storage device 102 or is output to an output device such as the display 107 via the display I / F 105.
- the input device 106 is used to input quality data and device information data, input parameters such as aggregation targets, comparison conditions, reference conditions and the like, and input requirements for starting quality data processing.
- the input data received by the input device 106 is stored in the auxiliary storage device 102 via the input I / F 104.
- the start request accepted by the input device 106 is input to the processor 101 via the input I / F 104.
- the data aggregation unit 1 acquires quality data and device information data.
- FIG. 3 is an example of quality data.
- pass / fail result representing pass / fail result
- temperature, vibration, rotational speed contact 1 current, contact 1
- the voltage, the contact 2 current, the contact 2 voltage, etc. are shown.
- data items such as temperature and vibration indicate the following. For example, in the test of a product having a rotating mechanism, a load is applied to the product, and measurement of a portion in accordance with the product specification is performed.
- the quality data is data indicating the state of the product which is the quality analysis target object
- the quality data is a set of values acquired each time the product is manufactured or inspected.
- the quality data may be recorded in any device, for example, data stored in a factory line device or a monitoring system for controlling the device. Alternatively, it may be data stored in a product management system that manages test results in product inspection.
- FIG. 4 shows an example of device information data.
- the device ID is identification information of each device
- the type ID is identification information indicating the type of the device
- the equipment ID is identification information indicating which type of device the device is configured.
- the setting list ID is information for identifying apparatus setting information such as a product manufacturing condition and a reference value (upper and lower limit values) used for product inspection.
- the apparatus information data is data indicating information of an apparatus handling a product as a quality analysis target object, and thus is a sequence of values acquired each time a product is manufactured or time series data.
- Time series data is a sequence of values obtained by sequentially observing as time passes.
- the time-series data may be any data, for example, time-series data stored in a control system for controlling a manufacturing apparatus such as a processing machine, a robot, or a pump. It may be data stored in equipment of a factory production line or test line.
- FIG. 3 and FIG. 4 describe the data as one table, the equipment and device data may be divided into a plurality of tables as long as the devices can be associated.
- the data type classification unit 2 stores, for each data item aggregated by the data aggregation unit 1, a name, an ID and the like capable of identifying the data item.
- the name or ID that can be identified for each data item may be analogized from the serial number or the value of the data item, or may be manually defined from the outside.
- FIG. 5 shows an example in which the data items aggregated by the data aggregation unit 1 are integrated to generate a table. This table may be a spreadsheet application sheet or a table in a database.
- FIG. 6 is a flowchart showing the operation of the condition setting unit 3 and the distribution difference calculation unit 4.
- FIG. 7 is an explanatory view showing the conditions set by the condition setting unit 3.
- the condition setting unit 3 sets an analysis condition as -Data items to be calculated as frequency distribution and their upper and lower limit values (totaling target)
- the data item to be the comparison condition, the value, the data item to be the reference condition, and three points of the value are selected (steps ST1 and ST2), and the selection result is set (step ST3).
- the comparison condition and the reference condition may be automatically classified as shown in FIG. 7 by a clustering method.
- the processor 101 When defining from the outside, the processor 101 performs processing corresponding to the condition setting unit 3 by inputting the corresponding value from the input device 106 in FIG. 2, and stores the analysis condition in the auxiliary storage device 102.
- the example shown in FIG. 7 is condition setting for extracting records that satisfy the conditions for each of vibration and rotational speed.
- An example query for obtaining the value of each data item is shown below.
- ⁇ Calculation target The value of "vibration” is 100 to 120, the value of "rotational speed” is 0 to 50
- aggregation unit is an aggregation unit of frequency distribution. It is a unit per scale of the horizontal axis in the frequency distribution of FIG. 8 described later. Furthermore, the conditions for which “2016/04”, “2016/06” and “OK” which are displayed shaded in “period”, “type ID”, “pass / fail result” and “temperature” of the display example are selected are shown.
- the distribution difference calculation unit 4 extracts data satisfying the “comparison condition” for each “data item to be aggregated” set by the condition setting unit 3 from the data collected by the data type classification unit 2, and the area is 1
- the frequency distribution is calculated so as to be (step ST4, step ST5).
- the frequency distribution satisfying this comparison condition is called a comparison distribution.
- data satisfying the “reference condition” is also extracted, and the frequency distribution is calculated so that the area is 1.
- the frequency distribution satisfying this reference condition is called a reference distribution. Then, the reference distribution and the comparison distribution are superimposed and output.
- step ST7 the reference distribution for each data item to be aggregated is The comparison distribution.
- FIG. 8 An example is shown in FIG. In FIG. 8, the horizontal axis represents the number, and the vertical axis represents the frequency.
- the solid line is the reference distribution, and the broken line is the comparative distribution.
- the reference 8000 and the comparison 2000 indicate that there are 8000 reference conditions and 2000 comparison conditions.
- the data item at which the difference between the reference distribution and the comparison distribution is the largest in the output of the distribution difference calculation unit 4 is the failure occurrence factor.
- the degree of deviation of “vibration” since the degree of deviation of “vibration” is high, it can be considered that “vibration” has a high possibility of causing a defect.
- the distance between the peak of the reference distribution and the peak of the comparison distribution may be calculated for each data item, rearranged in descending order of the degree of divergence, and output. Also, the number of samples of the reference distribution and the number of populations of each of the comparative distributions may be output.
- the quality analysis device it is possible to quantitatively and quickly extract the tendency of the quality data and the device information data regardless of the occurrence of the failure. For example, it is possible to quickly determine whether the current condition is normal or not by comparing the data of the period in which the product has been successfully manufactured after device maintenance with the latest data for several data items.
- the reference condition is "a normal operation period immediately after maintenance"
- the comparison condition is a "period for which the latest comparison is desired”.
- the present is 05/01/2016
- the maintenance is carried out on 04/01/2016, and one week after that, it has been operating normally without any problems.
- the reference condition is set to 2016/04/01 to 2016/04/08.
- the latest data used as the comparison condition a desired period other than the normal operation period is used. Also, if a data item in which the degree of deviation between the reference distribution and the comparison distribution is large is found, it is possible to cope with the problem before it occurs.
- the data aggregation unit acquires the quality data indicating the state of the quality analysis object and the device information data indicating the information of the device handling the quality analysis object And comparison between the data items to be aggregated, the reference conditions indicating the conditions to be the basis of the quality analysis target, and the conditions to be the quality analysis target, for the quality data and device information data acquired by the data aggregation unit Extract the data that satisfies the reference condition of the data item set by the condition setting unit and the data that satisfies the comparison condition from the condition setting unit that sets conditions and the quality data and device information data acquired by the data aggregation unit And the distribution difference calculating unit which calculates the frequency distribution for each data item and outputs data indicating the degree of deviation of the frequency distribution of the reference condition and the comparison condition, so that defect factor candidates can be extracted quickly. It can be, and it is possible to infer the occurrence of problems easily.
- the distribution difference calculation unit is provided with a data type classification unit that classifies the quality data and device information data acquired by the data aggregation unit according to the set type. Since the data classified by the data type classification unit is used instead of the quality data and the device information data acquired by the data aggregation unit, it is possible to more quickly extract the cause of the failure.
- the condition setting unit sets the data item, the reference condition, and the comparison condition based on the data item instructed from the outside, the reference condition, and the comparison condition. Therefore, arbitrary data items, reference conditions and comparison conditions can be easily set.
- the data aggregation unit acquires the quality data indicating the state of the quality analysis object and the device information data indicating the information of the apparatus handling the quality analysis object.
- a step, a condition setting unit, for the quality data and device information data acquired in the data aggregation step, a data item to be aggregated, a reference condition indicating a condition to be a basis of the quality analysis object, and a quality analysis object The condition setting step of setting the comparison condition indicating the condition to be set, and the distribution difference calculation unit, the reference condition of the data item set in the condition setting step from the quality data and device information data acquired in the data aggregation step Extract the data that satisfies and the data that satisfies the comparison condition, calculate the frequency distribution for each data item, and show the data that indicates the divergence between the frequency distribution of the reference condition and the comparison condition Since a distribution variance calculation step of force, the candidate of the insufficiency cause can be quickly extracted, and it is possible to infer the
- event data indicating what kind of event has occurred in the apparatus is also included, and the values of the data items obtained in the first embodiment and the event data
- the expert is a person who is familiar with the manufacturing process and the testing process of the product, and indicates, for example, an experienced worker, a designer of a manufacturing apparatus, and the like.
- FIG. 9 is a block diagram showing the quality analysis apparatus of the second embodiment.
- the illustrated quality analysis apparatus includes a data aggregation unit 1 a, a data type classification unit 2, a condition setting unit 3, a distribution difference calculation unit 4, and an event influence analysis unit 5. Similar to the data aggregation unit 1 in the first embodiment, the data aggregation unit 1a acquires quality data and device information data, and acquires event data indicating what kind of event has occurred for the device.
- the event influence analysis unit 5 treats a data item whose deviation degree in the distribution difference calculation unit 4 is equal to or more than a set value as a factor candidate having a possibility of malfunction, the relationship between the value of the factor candidate for a fixed period and the event occurrence date It is a processing unit that outputs the data shown.
- the data type classification unit 2 to the distribution difference calculation unit 4 have the same configuration as that of the first embodiment, so the corresponding parts are denoted by the same reference numerals and the description thereof is omitted.
- the hardware configuration of the quality analysis apparatus according to the second embodiment is the same as the configuration shown in FIG. However, in addition to the configuration of the first embodiment, the processor 101, the auxiliary storage device 102, and the memory 103 are configured to realize the function corresponding to the data aggregation unit 1a and the function corresponding to the event influence analysis unit 5. There is.
- FIG. 10 is an explanatory diagram of an example of event data.
- “facility ID”, “type ID”, “device ID”, “event occurrence date”, “event category”, “event detail”,... are shown as data items.
- the "event occurrence date” is the date and time when the event occurred
- the "event classification” is information indicating the type of the event.
- there are various types of events for each manufacturing site such as “maintenance of equipment”, “cleaning of equipment”, “change of supplier”, “change of material specification”, “change of person in charge”, etc. Polishing of the mold (one of the equipment maintenance) is “Event Category 1", and material exchange is “Event Category 2".
- “event details” is information indicating specific contents of an event.
- the processing of data indicating the degree of divergence of the data of the reference condition and the comparison condition based on the quality data and the device information data acquired by the data aggregation unit 1a is the same as that of the first embodiment.
- the processes of data type classification unit 2, condition setting unit 3, and distribution difference calculation unit 4 are the same as in the first embodiment, and thus the description thereof is omitted here.
- FIG. 11 is a flowchart showing the operation of the event influence analysis unit 5.
- the event influence analysis unit 5 sets a data item whose deviation degree is equal to or more than a set value as a factor candidate (step ST11).
- the relationship between the change in the tendency waveform of the factor candidate and the event data is extracted (step ST12).
- the tendency waveform of the factor candidate is, for example, a set of values as follows.
- Event influence analysis unit 5 analyzes both trend waveform and event data By correlating the date and equipment information common to the data, the change of the tendency waveform due to the event is output (step ST13).
- FIG. 12 is an example of the value of the factor candidate in the tendency waveform of the factor candidate
- FIG. 13 is a graph of the tendency waveform based on the values of FIG.
- the average daily vibration value changes in the first half of March, and the difference between the average daily vibration value and the previous day has a peak in the first half of March.
- the number of NG shows a peak in the middle of March.
- FIG. 14 is an example in which the trend waveform and the event data are associated with the closest date.
- FIG. 15 is the graph.
- the broken line indicates the event occurrence date.
- the vibration NG number increases immediately after the event occurrence date is 3/14, and it can be estimated that the 3/14 event is the cause of the failure occurrence.
- the trend waveform and the event data may be associated with each other by data items other than the event occurrence date.
- the event category it is possible to determine which event category (type of event) has changed the tendency.
- immediately after the occurrence of the event segment 1 it may be confirmed whether the trend waveform always has a similar change. If, after execution of a certain event, a trend change has occurred in a large number of sections, it is assumed that there is a case in which no change occurs in a certain section. If such a section or event is found, it can be inferred that there is something abnormal in that section or that the event itself is defective.
- the data aggregation unit acquires event data indicating what kind of event has occurred with respect to the device, and the degree of divergence of the distribution difference calculation unit Since the event influence analysis unit outputs data indicating the relationship between the value of a factor candidate for a fixed period and event data, as a factor candidate having a possibility that a problem is a data item having a value greater than the set value, As well as factors that change the tendency of the candidate, it is possible to know when the tendency changes and events associated with the change in the tendency. Thereby, the factor of the factor candidate can be identified quantitatively and quickly. In addition, by quantifying the change (impact) of the tendency of the factor candidate due to the event, it becomes possible to confirm the tendency after the planned event.
- the present invention allows free combination of each embodiment, or modification of any component of each embodiment, or omission of any component in each embodiment. .
- the quality analysis device and the quality analysis method according to the present invention relate to a configuration that enables estimation of the cause of the trend change in the manufacturing process and testing process of the product and the cause of the failure of the product. Suitable for estimating product defects under set conditions.
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Abstract
Description
不具合の要因を抽出するにあたり、製造現場の製造装置や試験装置に備わるセンサから得られた、製品の製造条件、試験条件、試験結果といった情報は製品の状態を表す品質データとしてこれを活用することは有効である。製造現場の装置は、多数のセンサで構成されており、それぞれのセンサでは電流や温度等、センサに応じた値が品質データとして取得される。ここでは電流や温度などセンサに対応する品質データの種類をデータ項目と呼ぶ。
しかしながら、従来の品質分析装置では、不具合の発生が前提となっており、不具合には至らないが徐々に変化する品質データの抽出は困難であった。仮に不具合が発生していても度数分布の比較による不具合要因の抽出は、度数分布を確認する分析者に依るため、データ項目の増加に伴い、確認の負荷も高くなる。また、度数分布に何らかの差が発生していたとしても、差が発生する原因の推定については作業者の経験で定性的に判断されている場合が多く、不具合の要因の特定は困難であった。
実施の形態1.
図1は、本実施の形態による品質分析装置の構成図である。
本実施の形態による品質分析装置は、データ集約部1、データ種類分類部2、条件設定部3、分布差異計算部4を備える。データ集約部1は品質データと装置情報データを取得する処理部である。データ種類分類部2は、データ集約部1で取得された品質データと装置情報データとを設定された所定のルールで分類する処理部である。条件設定部3は、データ集約部1で取得されたデータまたはデータ種類分類部2で分類されたデータに対して、集計対象となるデータ項目と、品質分析の基本となる条件を示す基準条件と、品質分析対象とする条件を示す比較条件とを設定する処理部である。分布差異計算部4は、データ集約部1で取得されたデータまたはデータ種類分類部2で分類されたデータから、条件設定部3で設定された条件を満たすデータを抽出して、データ項目毎に度数分布を計算し、基準条件と比較条件のデータの乖離度を示すデータを出力する処理部である。
図示の品質分析装置はコンピュータを用いて構成され、プロセッサ101、補助記憶装置102、メモリ103、入力インタフェース(入力I/F)104、ディスプレイインタフェース(ディスプレイI/F)105、入力装置106、ディスプレイ107、信号線108、ケーブル109,110を備えている。プロセッサ101は、信号線108を介して、他のハードウェアに接続されている。入力I/F104は、ケーブル109を介して、入力装置106に接続されている。ディスプレイI/F105は、ケーブル110を介して、ディスプレイ107に接続されている。
入力装置106は、品質データ及び装置情報データの入力、集計対象、比較条件、基準条件等のパラメータの入力、品質データ処理の開始要求等の入力に使用される。入力装置106が受け付けた入力データは、入力I/F104を介して、補助記憶装置102に記憶される。入力装置106が受け付けた開始要求は、入力I/F104を介して、プロセッサ101に入力される。
データ集約部1は、品質データと装置情報データを取得する。図3は品質データの一例である。図3では品質データのデータ項目の例として、製番、装置へ製品が投入された日時(=投入時刻)、合格不合格を表す合否結果、温度、振動、回転速度、接点1電流、接点1電圧、接点2電流、接点2電圧などを示している。ここで、温度、振動等のデータ項目は次のことを示している。例えば、回転機構を持つ製品の試験では、製品に負荷を与え、製品仕様に則った箇所の測定を行う。その測定では、負荷を与えた際の製品温度や振動、回転速度、所定の箇所(接点1、接点2等)を流れる電流電圧を測定する。図3におけるデータ項目の「接点1電流」や「接点1電圧」は、試験時の所定箇所における電流値及び電圧値を示している。
品質データは、品質分析対象物である製品の状態を示すデータであるため、製品の製造や検査の都度、取得された値の集合である。品質データはどのような装置に記録されているものでも良く、例えば工場のライン装置や、装置を制御するための監視システムに蓄積されているデータである。あるいは、製品検査における試験結果を管理する製品管理システムに蓄積されているデータでも良い。
条件設定部3は、分析条件として、
・度数分布として計算するデータ項目と、その上下限値(集計対象)
・比較条件とするデータ項目と、その値
・基準条件とするデータ項目と、その値
の3点を選択し(ステップST1、ST2)、その選択結果を設定する(ステップST3)。比較条件や基準条件は、クラスタリング手法により図7のように自動で分類しても良い。あるいは、予め人手で定義しておいても良いし、データベースのクエリのように条件を人手で記入しても良い。外部から定義する場合は、図2における入力装置106から対応する値を入力することでプロセッサ101が条件設定部3に対応する処理を行い、分析条件を補助記憶装置102に記憶させる。
■集計対象:「振動」の値が100~120、「回転速度」の値が0~50
■比較条件:期間2016/4,2016/6,合否結果=OK
・比較条件を満たす「振動」のクエリ
SELECT 振動 FROM データ種類分類部表
WHERE 振動BTWEEN 100 AND 120 AND
投入時刻=2016/4 OR 投入時刻=2016/6 AND
合否結果=OK
・比較条件を満たす「回転速度」のクエリ
SELECT 回転速度 FROM データ種類分類部表
WHERE 回転速度BTWEEN 0 AND 50 AND
投入時刻=2016/4 OR 投入時刻=2016/6 AND
合否結果=OK
■基準条件:全て
・基準条件を満たす「振動」のクエリ
SELECT 振動 FROM データ種類分類部表
WHERE 振動BTWEEN 100 AND 120
・基準条件を満たす「回転速度」のクエリ
SELECT 回転速度 FROM データ種類分類部表
WHERE 回転速度BTWEEN 0 AND 50
また、出力の際に、データ項目毎に、基準分布のピークと、比較分布のピークの距離を算出して、乖離度合いが高い順に並べ替えて出力しても良い。また、基準分布の標本数と、比較分布それぞれの母集団数を出力しても良い。
また、基準分布と比較分布の乖離度が大きくなっているデータ項目を発見した場合は、不具合に至る前に対応することができる。
実施の形態2は、データ集約部1が取得するデータとして、装置に関してどのようなイベントが発生したかを示すイベントデータも含めるようにし、実施の形態1で求めたデータ項目の値とイベントデータとの関係を求めるようにしたものである。
すなわち、分布差異計算部4で抽出された基準条件と比較条件の乖離度の高いデータ項目は、あくまでも品質データや装置情報データから統計的に求められた、不具合の可能性が高い事象(以降、これを要因候補という)である。そこで、実施の形態2では、従来は専門家が確認していたイベントデータと要因候補のOK/NGの変化や統計量で集約した値とを対応付ける。これによって、専門家の知見を反映させたのと同等の結果を得ることができる。なお、ここで専門家とは、製品の製造工程や試験工程について熟知した人物であり、例えば、経験豊富な作業者や製造装置の設計者等を指す。
先ず、データ集約部1aは、品質データと装置情報データに加えてイベントデータを取得する。
図10は、イベントデータの一例を示す説明図である。この例では、データ項目として「設備ID」「種別ID」「装置ID」「イベント発生日」「イベント区分」「イベント詳細」…が示されている。「イベント発生日」は、そのイベントが発生した日時であり、「イベント区分」とは、そのイベントの種類を示す情報である。例えば、「装置のメンテナンス」「装置の清掃」「調達先の変更」「材料仕様の変更」「担当者の変更」等、製造現場毎にさまざまな種類のイベントがあり、ここでは、例として金型の研磨(装置メンテナンスのひとつ)を「イベント区分1」、材料の交換を「イベント区分2」としている。また、「イベント詳細」とは具体的なイベントの内容を示す情報である。
先ず、イベント影響解析部5は、乖離度が設定値以上のデータ項目を要因候補として設定する(ステップST11)。次に、要因候補の傾向波形の変化とイベントデータとの関連性を抽出する(ステップST12)。要因候補の傾向波形は、例えば以下のような値の集合である。
・要因候補の値
・日毎や月毎等、一定期間で、要因候補の値を平均値や標準偏差等の統計量で集約した値
・日毎や月毎等、一定期間で集約した、要因候補のOK/NG数
・日毎や月毎等、一定期間で、要因候補の値を平均値や標準偏差等の統計量で集約した値の差分
イベント影響解析部5は、傾向波形とイベントデータを、両データに共通する日付や設備情報を元に対応付けすることで、イベントによる傾向波形の変化を出力する(ステップST13)。
図14は、傾向波形とイベントデータとを最も近い日付で対応付けした例である。図15は、そのグラフである。図15において、破線がイベント発生日を示している。この例では、図15に示すように、イベント発生日が3/14の直後に振動NG数が増加しており、3/14のイベントが不具合発生の要因と推定することができる。
Claims (5)
- 品質分析対象物の状態を示す品質データと、前記品質分析対象物を扱う装置の情報を示す装置情報データを取得するデータ集約部と、
前記データ集約部で取得された前記品質データと前記装置情報データに対して、集計対象となるデータ項目と、品質分析対象の基本となる条件を示す基準条件と、品質分析対象とする条件を示す比較条件とを設定する条件設定部と、
前記データ集約部で取得された前記品質データと前記装置情報データから、前記条件設定部で設定された前記データ項目の前記基準条件を満たすデータと前記比較条件を満たすデータとを抽出して、前記データ項目毎に度数分布を計算し、前記基準条件と前記比較条件の前記度数分布の乖離度を示すデータを出力する分布差異計算部とを備えたことを特徴とする品質分析装置。 - 前記データ集約部で取得された前記品質データと前記装置情報データを、設定された種類毎に分類するデータ種類分類部を備え、
前記分布差異計算部は、前記データ集約部で取得された前記品質データと前記装置情報データに代えて、前記データ種類分類部で分類されたデータを用いることを特徴とする請求項1記載の品質分析装置。 - 前記条件設定部は、外部より指示されたデータ項目と基準条件と比較条件とにより、前記データ項目と前記基準条件と前記比較条件とを設定することを特徴とする請求項1または請求項2記載の品質分析装置。
- 前記データ集約部は、装置に関してどのようなイベントが発生したかを示すイベントデータを取得し、かつ、
前記分布差異計算部の乖離度が設定値以上であるデータ項目を、不具合の可能性がある要因候補として、当該要因候補の一定期間の値と、前記イベントデータとの関係を示すデータを出力するイベント影響解析部を備えたことを特徴とする請求項1または請求項2記載の品質分析装置。 - データ集約部が、品質分析対象物の状態を示す品質データと、前記品質分析対象物を扱う装置の情報を示す装置情報データを取得するデータ集約ステップと、
条件設定部が、前記データ集約ステップで取得された前記品質データと前記装置情報データに対して、集計対象となるデータ項目と、品質分析対象の基本となる条件を示す基準条件と、品質分析対象とする条件を示す比較条件とを設定する条件設定ステップと、
分布差異計算部が、前記データ集約ステップで取得された前記品質データと前記装置情報データから、前記条件設定ステップで設定された前記データ項目の前記基準条件を満たすデータと前記比較条件を満たすデータとを抽出して、前記データ項目毎に度数分布を計算し、前記基準条件と前記比較条件の前記度数分布の乖離度を示すデータを出力する分布差異計算ステップとを備えたことを特徴とする品質分析方法。
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