以下，參照圖式對實施方式進行說明。
[監視系統的概要]
參照圖1，對本實施方式的監視系統1的概要進行說明。
圖1是示出監視系統1的一個例子的圖。
監視系統1進行與機器10的運轉狀態相關的監視。監視系統1包括機器10、控制裝置20、監視裝置30、以及終端裝置40。
機器10藉由批次過程(批次次處理)進行處理。機器10例如是在工廠等配置而在生產工序中使用的機器。例如，機器10包括在制紙工廠、印刷工廠等設置的送紙機械、在金屬衝壓工廠等設置的旋轉剪切機(剪切機械)、衝壓機械等。
在監視系統1中包含的機器10可以為一個，亦可以為複數個。
需要說明的是，除了機器10之外，監視系統1可以將設置於工廠的生產線等的複數個機器作為整體實施批次過程的設備(生產機器群)作為對象，進行其運轉狀態的監視。
控制裝置20對機器10的動作進行控制。具體而言、控制裝置20可以自機器10取得表示機器10的運轉狀態的各種測定資料，並且基於取得的測定資料，以符合規定的運轉條件(例如，規定的定序)的方式對機器10的動作進行控制。控制裝置20例如藉由一對一的通信線、工廠內的現場網路等的通信線路，以能夠進行通信的方式與機器10連接。控制裝置20例如為PLC(Programmable Logic Controller)、邊緣處理器等。
控制裝置20可以針對每個控制對象的機器10設置一個，亦可以針對控制對象的複數個機器設置一個。即，在監視系統1包含的控制裝置20可以為一個，在監視系統1中包含複數個機器10的情況下，可以為複數個。
監視裝置30(診斷裝置的一個例子)進行與機器10的運轉狀態相關的監視。具體而言，監視裝置30可以進行與機器10的運轉狀態的異常相關的診斷。
與機器10的運轉狀態的異常相關的診斷例如包括機器10的運轉狀態的異常的有無的診斷。另外，與機器10的運轉狀態的異常相關的診斷例如包括符合機器的運轉狀態的異常的程度(以下，稱為“異常度”)的診斷。
監視裝置30可以針對監視對象的每一個機器10設置一個，亦可以針對監視對象的複數個機器10設置一個。即，在監視系統1中包含的監視裝置30可以為一個，在監視系統1中包含複數個機器10的情況下，可以為複數個。
監視裝置30藉由規定的通信線路，自規定的每個採樣週期的控制裝置20取得表示機器10的運轉狀態的各種資料(以下，稱為“運轉資料”)，藉由監視取得的運轉資料，進行與機器10的異常相關的診斷。採樣週期以例如數百毫秒～數十秒的範圍進行規定。
運轉資料中包括例如藉由控制裝置20自機器10取得的、表示機器10的運轉狀態的各種測定資料。另外，運轉資料中包括例如為了機器10的控制而由控制裝置20生成的控制指令等的控制資料。運轉資料包括例如表示機器10的規定的部位的溫度、壓力、轉矩、流量等的機器10的複數個種類的狀態變數(以下，稱為“過程變數”)的資料。因此，運轉資料例如表示為用於表示複數個種類的狀態(過程變數)的向量資料。
規定的通信線路包括例如一對一的通信線。另外，規定的通信線路例如包括在機器10以及控制裝置20所設置的工廠等的設施中設置的現場網路等的局域網(LAN：Local Area Network)。另外，規定的通信線路包括例如廣域網路(WAN：Wide Area Network)。廣域網路包括例如以基站為末端的移動通信網、利用通信衛星的衛星通信網、以及網際網路等。另外，規定的通信線路包括例如利用規定的無線通訊方式的近距離通信線路。近距離通信線路包括例如依據WiFi、藍牙(注冊商標)等的通信方式的無線通訊線路。
監視裝置30是例如在與機器10以及控制裝置20所設置的工廠等的設施相同的設施、用地內設置的終端裝置。終端裝置是例如PLC、臺式PC(Personal Computer)等的定置型的終端裝置。另外，終端裝置可以是例如智慧手機、平板電腦終端、膝上型的PC等的可搬運型的終端裝置(攜帶終端)。另外，監視裝置30例如是伺服器裝置。伺服器裝置例如是在機器10以及控制裝置20所設置的工廠等的設施的用地的外部設置的本地部署伺服器、雲端伺服器。另外，伺服器裝置例如是機器10以及控制裝置20可以是在設置的工廠等的設施的用地內或其近鄰設置的邊緣伺服器。
需要說明的是，監視裝置30可以直接自機器10取得運轉資料。
終端裝置40藉由規定的通信線路以能夠進行通信的方式與監視裝置30連接，其是將與監視裝置30的監視結果相關的資訊提供給使用者的使用者終端。
終端裝置40可以是例如台式PC等的定置型的終端裝置，亦可以是智慧手機、平板電腦終端、膝上型的PC等的攜帶終端。
[監視裝置的硬體構成]
接下來，參照圖2，對本實施方式的監視裝置30的硬體構成進行說明。
圖2是示出監視裝置30的硬體構成的一個例子的圖。
監視裝置30的功能藉由任意的硬體或任意的硬體和軟體的組合等來實現。例如，如圖2所示，監視裝置30包括藉由匯流排B連接的外部端子31、輔助記憶裝置32、記憶體裝置33、CPU(Central Processing Unit)34、通訊連接端子35、輸入裝置36、以及顯示裝置37。
外部端子31作為用於自記錄介質31A讀取資料、向記錄介質31A的寫入資料的端子起作用。記錄介質31A例如包括軟碟、CD(Compact Disc)、DVD(Digital Versatile Disc)、BD(Blu-ray(注冊商標) Disc)、SD記憶卡、USB (Universal Serial Bus)記憶體等。由此，監視裝置30藉由記錄介質31A讀取在處理中利用的各種資料，並且儲存於輔助記憶裝置32中，從而能夠安裝用於實現各種功能的程式。
需要說明的是，監視裝置30可以藉由通訊連接端子35自外部裝置取得各種資料、程式。
輔助記憶裝置32用於儲存安裝的各種程式，並且用於儲存各種處理所需的檔、資料等。輔助記憶裝置32例如包括HDD(Hard Disc Drive)、SSD(Solid State Drive)等。
記憶體裝置33在存在程式的起動指令的情況下，自輔助記憶裝置32讀取並儲存程式。記憶體裝置33例如包括DRAM(Dynamic Random Access Memory)、SRAM(Static Random Access Memory)。
CPU34執行自輔助記憶裝置32載入記憶體裝置33的各種程式，並且依照程式實現與監視裝置30相關的各種功能。
通訊連接端子35作為用於以能夠進行通信的方式與外部機器連接的端子來使用。由此，監視裝置30藉由通訊連接端子35以能夠進行通信的方式與例如控制裝置20以及終端裝置40等的外部機器連接。另外，通訊連接端子35可以根據與連接的機器之間的通信方式等而具有複數個種類的通訊連接端子。
輸入裝置36自使用者接受各種輸入。
輸入裝置36例如包括接受自用戶的機械操作輸入的操作輸入裝置。操作輸入裝置例如包括按鈕、撥簧開關、拉杆等。另外，操作輸入裝置例如包括安裝於顯示裝置37的觸控式螢幕幕、與顯示裝置37分開設置的觸控板等。
另外，輸入裝置36例如包括能夠接受來自用戶的聲音輸入的聲音輸入裝置。聲音輸入裝置例如包括能夠收集使用者的聲音的麥克風。
另外，輸入裝置36例如包括能夠接受來自用戶的手勢輸入的手勢輸入裝置。手勢輸入裝置例如包括能夠對使用者的手勢的樣子進行攝像的相機。
另外、輸入裝置36例如包括能夠接受來自用戶的活體輸入的活體輸入裝置。活體輸入裝置例如包括能夠取得包含與用戶的指紋、虹膜相關的資訊的圖像資料的相機。
顯示裝置37在監視裝置30的控制下向使用者顯示資訊畫面、操作畫面。顯示裝置37例如包括液晶顯示器、有機EL(Electroluminescence)顯示器等。
[監視裝置的功能構成]
接下來，參照圖3～圖10，對本實施方式的監視裝置30的功能構成進行說明。
圖3是示出監視裝置30的功能構成的一個例子的功能框圖。圖4是示出每個批次過程的運轉資料的一個例子的圖。圖5是示出批次資料的一個例子的示意圖。圖6是示出資料轉換的方法的一個例子的示意圖。圖7～圖9是示出批次過程中的機器10的運轉資料的正常的狀態的變化的第一例～第三例的圖。圖10是用於說明正常模型的更新條件的一個例子的圖。
如圖3所示，監視裝置30包括批次資料記憶部301、資料記憶部302、模型生成處理部303、正常模型記憶部304、診斷處理部305、以及模型變更處理部306。批次資料記憶部301以及正常模型記憶部304的功能例如藉由在輔助記憶裝置32中規定的記憶區域來實現。另外，資料記憶部302、模型生成處理部303、診斷處理部305、以及模型變更處理部306的功能例如藉由在輔助記憶裝置32中安裝的程式載入於記憶體裝置33中而在CPU34上被執行來實現。
批次資料記憶部301中記憶自控制裝置20接收的、機器10的每個批次過程的時間序列的運轉資料(以下，稱為“批次資料”)。
例如，如圖4所示，在運轉資料中，例如包括針對溫度、壓力、轉矩、流量等的每個過程變數且針對每個採樣週期的狀態資料。並且，在批次過程中，相同種類(變數)的狀態資料針對每個批次表示類似的波形(曲線圖)。
例如，如圖5所示，批次資料表示為針對每個批次i，針對每個批次i中的自批次開始時的經過的時間k，且針對每個過程變數j的三維資料。批次i表示1以上且記憶的批次數量I以下的整數，時間k表示1以上且在批次內的採樣次數K以下的整數，過程變數j表示1以上且過程變數的種類數量J以下的整數。以下，存在使用批次過程i、時間k、以及過程變數j將批次資料表示為x(i,j,k)的情況。
返回圖3，資料記憶部302使批次資料記憶於批次資料記憶部301中。具體而言，資料記憶部302將機器10的運轉狀態處於正常的範圍的情況的批次資料記憶於批次資料記憶部301中。
模型生成處理部303進行用於生成表示機器10的正常的運轉狀態的正常模型的處理。模型生成處理部303包括資料取得部3031、預處理部3032、資料轉換部3033、以及模型生成部3034。
資料取得部3031作為用於生成正常模型的基礎資料，自批次資料記憶部301取得機器10的運轉狀態相當於正常的狀態的批次資料。
預處理部3032對於藉由資料取得部3031取得的批次資料x(i,j,k)，進行規定的預處理，輸出預處理完成的批次資料x
s(i,j,k)。
預處理部3032例如對藉由資料取得部3031取得的批次資料實施標準化處理。具體而言，預處理部3032可以使用藉由資料取得部3031取得的、批次資料x(i,j,k)的複數個批次i之間的平均μj,k、標準差σj,k，進行批次資料x(i,j,k)的標準化處理。
資料轉換部3033將由三維形式表的、基於預處理部3032的預處理完成的批次資料x
s(i,j,k)轉換為二維形式的批次資料X
s(j,k)。
例如，如圖6所示，資料轉換部3033將批次資料x
s(i,j,k)分解為針對每個批次i的批次數I的批次資料群x
s(1,j,k)、x
s(2,j,k)、･･･、x
s(I,j,k)。並且，資料轉換部3033藉由在時間k的軸方向上結合分解後的批次資料群，生成相當於J行I･K列的矩陣資料的批次資料X
s(j,k)。
返回圖3，模型生成部3034(生成部的一個例子)基於藉由資料取得部3031取得的、表示機器10的正常的狀態的批次資料x
s(i,j,k)進行機器學習，生成表示機器10的正常的狀態的正常模型。
模型生成部3034例如作為正常模型生成藉由主成分分析(PCA：Principal Component Analysis)獲得的載荷矩陣(Loading Matrix)。
需要說明的是，在監視系統1包括複數個機器10的情況下，針對複數個機器10的每一個生成正常模型。另外，模型生成部3034可以藉由任意的方法生成正常模型。例如，模型生成部3034可以代替主成分分析而使用獨立成分分析(ICA：Independent Component Analysis)來生成正常模型。另外，例如，模型生成部3034可以應用支援向量機(SVM：Support Vector Machine)、深度神經網路(DNN：Deep Neural Network)等來生成正常模型。
在正常模型記憶部304中記憶藉由模型生成部3034生成的正常模型。另外，如後所述，在藉由模型生成部3034更新正常模型的情況下，在正常模型記憶部304中記憶更新後的正常模型，並且亦保存更新前的正常模型。具體而言，在正常模型記憶部304中，記憶在藉由診斷處理部305進行的與機器10的運轉狀態的異常相關的診斷中使用的正常模型的區域(位址)與記憶更新前的正常模型的區域(位址)被區別開來。
診斷處理部305進行用於與機器10的運轉狀態的異常相關的診斷的處理。診斷處理部305包括資料取得部3051、預處理部3052、指標值運算部3053、診斷部3054、以及通知部3055。
資料取得部3051取得自控制裝置20導入的、診斷對象的機器10的運轉資料
預處理部3052對於藉由資料取得部3051取得的運轉資料，進行與預處理部3032的情況相同的預處理。
指標值運算部3053基於完成藉由預處理部3052進行的預處理的機器10的運轉資料、以及藉由正常模型記憶部304記憶的最新的正常模型，對用於進行與機器10的運轉狀態的異常相關的診斷的規定的指標值進行運算。
指標值運算部3053例如基於完成預處理的機器10的運轉資料、以及作為正常模型的載荷矩陣，作為規定的指標值計算Q統計量以及T統計量。另外，指標值運算部3053例如可以作為規定的指標值計算基於機器10的自此次批次過程的開始的整個批次過程的Q統計量以及T統計量的各自的函數值。
診斷部3054基於由指標值運算部3053計算的指標值，進行與機器10的運轉狀態的異常相關的診斷。
診斷部3054在例如作為指標值的Q統計量以及T
2統計量的至少一者超過規定基準(以下，“異常徵兆基準”)IVth1的情況下，診斷為存在機器10的運轉狀態的異常的徵兆。另外，診斷部3054例如在作為指標值的Q統計量以及T
2統計量的至少一者超過比異常徵兆基準IVth1大的規定基準(以下，稱為“異常產生基準”)IVth2的情況下，診斷為機器10的運轉狀態存在異常。在該情況下，對於異常徵兆基準IVth1、異常產生基準IVth2，Q統計量的情況和T
2統計量的情況可以相同，亦可以不同。另外，診斷部3054例如可以如下診斷機器10的運轉狀態的異常度，即，作為指標值的Q統計量、T
2統計量越大，則機器10的運轉狀態的異常度越高。
另外，診斷部3054例如在作為指標值的此次整個批次過程的基於Q統計量的函數值、以及基於T
2統計量的函數值的至少一者超過異常徵兆基準IVth1的情況下，診斷為存在機器10d的運轉狀態陷入異常的徵兆。另外，診斷部3054例如在作為指標值的此次整個批次過程的基於Q統計量的函數值、以及基於T
2統計量的函數值的至少一者超過異常產生基準IVth2的情況下，判定機器10的運轉狀態存在異常。在該情況下，異常徵兆基準IVth1、異常產生基準IVth2可以在此次整個批次過程的基於Q統計量的函數值的情況與基於T
2統計量的函數值的情況下相同，亦可以不同。另外，診斷部3054例如可以以如下方式診斷機器10的運轉狀態的異常度，即，作為指標值的此次整個批次過程的基於Q統計量、T
2統計量的函數值越大，則機器10的運轉狀態的異常度越高。
通知部3055面向用戶將基於診斷部3054的診斷結果通知用戶。通知部3055例如藉由顯示裝置37向使用者通知診斷結果。另外，通知部3055例如可以藉由通訊連接端子35將診斷結果發送至終端裝置40，藉由在終端裝置40的顯示器中顯示診斷結果，向使用者通知診斷結果。
模型變更處理部306進行用於變更在基於診斷處理部305的與機器10的運轉狀態的異常相關的診斷中使用的正常模型的處理。模型變更處理部306包括變更指令部3061和設定部3062。
變更指令部3061生成並輸出用於變更由診斷處理部305使用的正常模型的指令。變更指令部3061包括變更指令部3061A、3061B。
變更指令部3061A生成用於對由診斷處理部305使用的正常模型自動進行更新的指令(以下，為了方便稱為“模型更新指令”)，並將其輸入模型生成處理部303。
例如，如圖7所示，作為機器10的旋轉剪切機在周邊的氣溫較低的情況下，與氣溫較高的情況相比轉矩相對變大。這是由於使用的潤滑油脂的粘度根據溫度變化。因此，隨著時間的經過，若旋轉剪切機的周邊的氣溫變化，則其轉矩的正常的範圍變化。
另外，例如，如圖8所示，作為機器10的送紙機械在周邊的濕度較高的情況下，與濕度較低的情況相比轉矩相對變大。這是由於隨著濕度的變化，紙的吸濕程度變化，其結果，輸送對象的紙的重量變化。因此，隨著時間的經過，若送紙機械的周邊的濕度變化，則其轉矩的正常的範圍變化。
另外，例如，如圖9所示，作為機器10的衝壓機械在周邊的氣溫較高的情況下，與氣溫較低的情況相比轉矩相對變大。這是由於隨著氣溫的變化，模具膨脹或收縮，其結果，模具的鋒利度變化。因此，隨著時間的經過，若衝壓機械的周邊的氣溫變化，則其轉矩的正常的範圍變化。
因此，變更指令部3061A可以與機器10的運轉狀態的正常的範圍的變化相應地將模型更新指令輸出至模型生成處理部303，使模型生成處理部303更新正常模型。
具體而言，若能夠判斷機器10的運轉狀態的正常的範圍自現在使用的正常模型超過偏移規定基準的規定的條件(第一條件的一個例子)(以下，稱為“模型更新條件”)成立，則變更指令部3061A可以輸出模型更新指令。模型更新條件可以為一個，亦可以為複數個，在規定了複數個模型更新條件的情況下，變更指令部3061A可以在複數個模型更新條件中的任一者成立的情況下生成模型更新指令，並將其輸出至模型生成處理部303。
模型更新條件例如是“相對於現在的正常模型，事後取得的機器10的批次資料相對較大偏離”。事後取得的機器10的批次資料是指，在比為了生成由診斷處理部305現在使用的正常模型而使用的批次資料靠後的定時取得的機器10的批次資料。
具體而言，模型更新條件可以是“指標值相對變大”。更具體而言，例如，如圖10所示，模型更新條件可以是“指標值超過規定基準(以下，稱為“模型更新基準”)IVth3”(參照圖中的虛線包圍部分)。另外，模型更新條件可以是“指標值的移動平均值超過模型更新基準IVth3”。另外，模型更新條件可以是“在此次批次過程內，指標值超過模型更新基準IVth3的比率RT超過規定基準(以下，稱為“模型更新基準”)RTth”。另外、模型更新條件例如可以是“指標值超過模型更新基準IVth3的狀態的連續次數CN超過規定基準(以下，稱為“模型更新基準”)CNth”。模型更新基準IVth3例如可以規定為比異常徵兆基準IVth1以及異常產生基準IVth2小的範圍，或者可以規定為異常徵兆基準IVth1與異常產生基準IVth2之間的範圍。另外，如圖10所示，模型更新基準IVth3可以與異常徵兆基準IVth1相同。
另外，模型更新條件例如可以是“自規定的起算點的由機器10生產的物品的生產數量PN超過規定基準(以下，稱為“模型更新基準”)PNth”。與基於機器10的物品的生產數量PN相關的資訊可以自控制裝置20取得。在該情況下，生產數量PN的起算點例如可以是現在使用的正常模型的使用開始時，亦可以是用於現在使用的正常模型的生成的批次資料的取得結束時。
另外，模型更新條件例如包括“自規定的起算點的經過時間Tm超過規定基準(以下，稱為“模型更新基準”)Tm_th”。經過時間Tm的起算點例如可以是現在使用的正常模型的使用開始時，亦可以是用於現在使用的正常模型的生成的批次資料的取得結束時。
另外，模型更新條件例如包括“機器10的設置場所的環境條件的變化超過規定基準(以下，稱為“模型更新基準”)。具體而言，模型更新條件可以包括“機器10的周邊的溫度Tp的變化量ΔTp超過規定基準(以下，稱為“模型更新基準”)ΔTp_th。另外，模型更新條件可以包括“機器10的周邊的濕度H的變化量ΔH超過規定基準(以下，稱為“模型更新基準”)ΔHth。機器10的周邊的溫度Tp、濕度H等的機器10的設置場所的環境條件例如由設置於機器10、機器10的周邊的感測器進行測定，與機器10的設置場所的環境條件相關的資訊藉由控制裝置20被導入監視裝置30。機器10的周邊的溫度Tp的變化量ΔTp、濕度H的變化量ΔH等的機器10的設置場所的環境條件的變化的起算點例如可以是現在使用的正常模型的使用開始時，亦可以是用於現在使用的正常模型的生成的批次資料的取得結束時。
另外，模型更新指令中可以包括與正常模型的更新方法相關的指令內容。
例如，作為模型的更新方法，設有複數個選項，自複數個選項之中藉由設定的更新方法對正常模型進行更新即可。
模型的更新方法可以包括例如使用最近的規定數量BN(例如，20個)的機器10在正常的狀態下的批次資料對正常模型進行更新的方法。機器10在正常的狀態下的批次資料是指，機器10的運轉狀態被診斷部3054診斷為正常的批次資料。具體而言，模型生成處理部303使用去除了機器10的運轉狀態被診斷為異常的批次資料的、自模型更新條件的成立時追溯最近的規定數量BN的機器10在正常的狀態下的批次資料，生成新的正常模型。
需要說明的是，模型生成處理部303可以在機器10的運轉狀態為正常的狀況持續的前提下，針對每次批次過程的結束，使用最近的規定數量的批次資料，生成新的正常模型，對由診斷處理部305使用的正常模型進行更新。具體而言，可以藉由代替為了上次正常模型的生成而使用的規定數量BN的批次資料中的最早的批次資料，使用包括最新的批次資料的新的規定數量BN的批次資料，對正常模型進行更新。該情況下的模型更新條件為“機器10的批次過程更新”。
另外，模型更新方法可以包括例如藉由將現在使用的用於正常模型的生成的批次資料中的一定數量或一定比率置換為事後取得的、機器10在正常的狀態下的批次資料，從而對正常模型進行更新。事後取得的批次資料是指，在用於現在使用的正常模型的生成的批次資料被取得的時間點之後取得的批次資料。具體而言，模型生成處理部303使用將用於現在使用的正常模型的生成的規定數量BN的批次資料中的一定數量或一定比率的批次資料置換為事後取得的、在機器10的正常的運轉狀態下的批次資料的新的規定數量BN的批次資料，生成新的正常模型。在該情況下，新追加的批次資料(組)可以是自最新的批次資料追溯而選擇的最近的批次資料(組)，亦可以是依據其之外的某種條件選擇的批次資料(組)。
變更指令部3061B生成廢棄由診斷處理部305現在使用的正常模型，返回最近的更新之前的正常模型的指令(以下，為了方便稱為“舊模型復活指令”)，並將其輸出至正常模型記憶部304。具體而言，變更指令部3061B根據舊模型復活指令，消除(廢棄)正常模型記憶部304的最新的正常模型，並且使最近的更新之前的正常模型移動至被診斷處理部305使用的正常模型的位址。由此，診斷處理部305會訪問更新前的正常模型，使用更新前的正常模型，從而進行與機器10的運轉狀態的異常相關的診斷。
例如，變更指令部3061B在現在使用的正常模型與實際的機器10的運轉狀態的正常的範圍之間的偏離超過規定基準(以下，稱為“舊模型復活基準”)的情況下，向正常模型記憶部304輸出舊模型復活指令。具體而言，若能夠判斷現在使用的正常模型與實際的機器10的運轉狀態的正常的範圍之間的偏離超過舊模型復活基準的規定的條件(以下，稱為“舊模型復活條件”)(第二條件的一個例子)成立，則變更指令部3061B可以輸出舊模型復活指令。舊模型復活條件可以為一個，亦可以為複數個，在規定了複數個舊模型復活條件的情況下，若複數個舊模型復活條件中的任一者成立，則變更指令部3061B可以生成舊模型復活指令，並將其輸出至正常模型記憶部304。
舊模型復活條件例如為“在最近的正常模型的更新的前後，機器10的運轉狀態被診斷部3054診斷為異常的頻率Fq超過規定基準(以下，稱為“舊模型復活基準”)Fq_th”。
另外，舊模型復活條件可以是例如“在最近的正常模型的更新的前後，基於診斷部3054的診斷結果表示出向自機器10的運轉狀態的正常的範圍偏離的方向的超過了規定基準(以下，稱為“舊模型復活基準”)的變化”。具體而言，舊模型復活條件可以是“在最近的正常模型的更新的前後，指標值的移動平均值IVm的増加量ΔIVm超過規定基準(以下，稱為“舊模型復活基準”)ΔIVm_th”。
設定部3062根據來自使用者的輸入，進行與正常模型的變更(更新或復活)相關的設定。來自用戶的輸入例如由輸入裝置36接受。另外，來自使用者的輸入例如藉由終端裝置40進行，藉由自終端裝置40接受表示使用者的輸入的信號，藉由通訊連接端子35(第一輸入部、第二輸入部、第三輸入部的一個例子)被接受。設定部3062包括設定部3062A～3062C。
設定部3062A(第一設定部的一個例子)根據來自使用者的規定的輸入，進行與模型更新條件相關的設定。例如，使用者可以藉由在顯示裝置37、終端裝置40的顯示器中顯示的規定的GUI(Graphical User Interface)，進行與模型更新條件相關的設定輸入。
設定部3062A例如根據來自使用者的規定的輸入，對模型更新基準IVth3，RTth，CNth，PNth，Tm_th，ΔTp_th，ΔHth等進行設定。用戶可以是能夠藉由設定部3062A直接設定模型更新基準IVth3、RTth、CNth、PNth、Tm_th、ΔTp_th、ΔHth等，亦可以是能夠間接設定。能夠直接設定是指，用戶能夠藉由設定輸入指定相當於模型更新基準IVth3、RTth、CNth、PNth、Tm_th、ΔTp_th、ΔHth等的值的狀態。能夠間接設定是指，用戶能夠藉由設定輸入指定用於決定相當於模型更新基準IVth3、RTth、CNth、PNth、Tm_th、ΔTp_th、ΔHth等的值的關係式之中的變數等的狀態。
設定部3062B(第二設定部的一個例子)根據來自使用者的規定的輸入，進行與正常模型的更新方法相關的設定。例如，使用者可以藉由在顯示裝置37、終端裝置40的顯示器中顯示的規定的GUI，進行與正常模型的更新方法相關的設定輸入。
設定部3062B例如可以根據來自使用者的規定的輸入，自複數個模型的更新方法之中選擇設定一個模型的更新方法。另外，設定部3062B例如可以根據來自使用者的規定的輸入，設定用於正常模型的更新的批次資料的數量(規定數量BN)。另外，設定部3062B可以設定用於正常模型的更新的批次資料中的、事後取得的批次資料的數量、比率等。
設定部3062C(第三設定部的一個例子)根據來自使用者的規定的輸入，進行與舊模型復活條件相關的設定。例如，使用者可以藉由在顯示裝置37、終端裝置40的顯示器中顯示的規定的GUI，進行與舊模型復活條件相關的設定輸入。
設定部3062C例如根據來自使用者的規定的輸入，對舊模型復活基準Fq_th，ΔIVm_th等進行設定。用戶可以是能夠藉由設定部3062C直接設定舊模型復活基準Fq_th、ΔIVm_th等，亦可以是能夠間接設定。
[正常模型的生成處理]
接下來，參照圖11，對基於監視裝置30(模型生成處理部303)的正常模型的生成處理進行說明。
圖11是概略示出基於模型生成處理部303的正常模型的生成處理的一個例子的流程圖。
本流程圖例如根據來自使用者的規定的輸入(請求)而執行。另外，本流程圖例如自模型變更處理部306(變更指令部3061A)輸出模型更新指令後執行。
如圖11所示，藉由步驟S102，資料取得部3031自批次資料記憶部301取得用於生成正常模型的相當於機器10的正常的狀態的批次資料(訓練資料)。
模型生成處理部303在步驟S102的處理完成後，進入步驟S104。
藉由步驟S104，預處理部3032對於在步驟S102中取得的批次資料進行規定的預處理。
模型生成處理部303在步驟S104的處理完成後，進入步驟S106。
藉由步驟S106，資料轉換部3033將在步驟S104中完成預處理的批次資料轉換為二維形式的批次資料。
模型生成處理部303在步驟S106的處理完成後，進入步驟S108。
藉由步驟S108，模型生成部3034基於在步驟S106中轉換為二維形式的批次資料，生成正常模型。如上所述，生成的正常模型記憶於正常模型記憶部304中。
模型生成處理部303在步驟S108的處理完成後，結束此次流程圖的處理。
如此，監視裝置30能夠基於相當於機器10的正常的運轉狀態的批次資料，生成正常模型。另外，監視裝置30可以在被診斷處理部305使用的正常模型需要更新的定時，根據模型更新指令，對正常模型進行更新。
[與機器的運轉狀態的異常相關的診斷處理]
接下來，參照圖12，對藉由監視裝置30(診斷處理部305)進行的與機器10的運轉狀態的異常相關的診斷處理進行說明。
圖12是概略示出藉由診斷處理部305進行的與機器10的運轉狀態的異常相關的診斷處理的一個例子的流程圖。
本流程圖例如在機器10的批次過程的開始至結束之間，針對每個規定的處理週期重複執行。批次過程的開始、結束例如藉由自控制裝置20發送且由監視裝置30接收的、表示機器10的批次過程的開始、結束的信號進行把握。
在本例中，使用表示機器10的運轉狀態的異常的有無的標誌F。標誌F在機器10的批次過程的開始時被初始化為表示不存在異常的狀態的“0”。
如圖12所示，藉由步驟S202，資料取得部3051取得被導入監視裝置30中的、機器10的最新的運轉資料。
診斷處理部305在完成步驟S202的處理後，進入步驟S204。
藉由步驟S204，預處理部3052對於在步驟S202中取得的運轉資料進行規定的預處理。
診斷處理部305在步驟S204的處理完成後，進入步驟S206。
藉由步驟S206，指標值運算部3053基於在步驟S204中完成預處理的最新的運轉資料、以及正常模型，計算指標值。
診斷處理部305在步驟S206的處理完成後，進入步驟S208。
藉由步驟S208，診斷部3054基於在步驟S206中計算的指標值，進行與機器10的運轉狀態相關的診斷。
診斷處理部305在步驟S208的處理完成後，進入步驟S210。
藉由步驟S210，診斷部3054判定步驟S208的診斷結果是否存在異常。診斷部3054在診斷結果存在異常的情況下，進入步驟S212，在不存在異常的情況下，進入步驟S216。
藉由步驟S212，通知部3055向用戶通知表示機器10的運轉狀態存在異常的診斷結果。
與診斷結果相關的通知的內容例如可以僅是機器10的運轉狀態存在異常的事實，亦可以除了該事實之外還包括成為該事實(診斷結果)的依據的資訊。成為診斷結果的依據的資訊例如包括表示指標值的時間序列的變化的圖表等的資訊等。以下，對於後述步驟S216的通知的內容亦可以相同。
診斷處理部305在步驟S212的處理完成後，進入步驟S214。
藉由步驟S214，診斷處理部305將標誌F設定為表示機器10的運轉狀態存在異常的“1”(F=1)。由此，監視裝置30(後述資料記憶部302)藉由確認標誌F，能夠判定特定的批次過程的批次資料是否表示機器10的正常的狀態(參照圖13)。
診斷處理部305在步驟S214的處理完成後，結束此次流程圖的處理。
另一方面，藉由步驟S216，通知部3055向用戶藉由與機器10的運轉狀態相關的診斷結果。具體而言，3055向用戶通知表示機器10的運轉狀態不存在異常、或者存在異常的徵兆等的診斷結果。
診斷處理部305在步驟S216的處理完成後，結束此次流程圖的處理。
如此，監視裝置30可以使用表示機器10的正常的運轉狀態的正常模型，線上診斷機器10的運轉狀態，並且將診斷結果通知用戶。
[批次資料的記憶處理]
接下來，參照圖13，對藉由監視裝置30(資料記憶部302)進行的表示機器10的正常的運轉狀態的批次資料的記憶處理進行說明。
圖13是概略示出藉由資料記憶部302進行的批次資料的記憶處理的一個例子的流程圖。
本流程圖例如在機器10的批次過程結束後執行。
如圖13所示，資料記憶部302判定標誌F是否為表示機器10的運轉狀態不存在異常的“0”。資料記憶部302在標誌F為“0”的情況下，進入步驟S304，在標誌F不為“0”，即為表示機器10的運轉狀態存在異常的“1”的情況下，結束此次流程圖的處理。
藉由步驟S304，資料記憶部302例如將在記憶體裝置33等中保存(緩衝)的、此次批次過程的開始至結束的時間序列的運轉資料作為批次資料保存於批次資料記憶部301中。
資料記憶部302在步驟S304的處理完成後，結束此次流程圖的處理。
如此，監視裝置30能夠僅記憶自控制裝置20導入的機器10的批次資料中的、被診斷處理部305診斷為運轉狀態不存在異常的機器10的批次資料。因此，監視裝置30可以使用用於現在使用的正常模型的生成的批次資料的取得完成後被記憶的批次資料，對正常模型進行更新。
[正常模型的變更處理]
接下來，參照圖14，對藉由監視裝置30(模型變更處理部306)進行的、被診斷處理部305使用的正常模型的變更處理進行說明。
圖14是概略示出藉由模型變更處理部306(變更指令部3061)進行的正常模型的變更處理的一個例子的流程圖。
本流程圖例如在機器10的批次過程結束後執行。
如圖14所示，變更指令部3061取得用於判定現在使用的正常模型是否需要變更、即模型更新條件和/或舊模型復活條件是否成立的最新的資料。
變更指令部3061在步驟S402的處理完成後，進入步驟S404。
藉由步驟S404，變更指令部3061A判定模型更新條件是否成立。變更指令部3061在模型更新條件成立的情況下，進入步驟S406，在模型更新條件不成立的情況下，進入步驟S408。
藉由步驟S406，變更指令部3061A將模型更新指令發送至模型生成處理部303，藉由模型生成處理部303，對由診斷處理部305使用的正常模型進行更新。
變更指令部3061在步驟S406的處理完成後，結束此次處理。
另一方面，藉由步驟S408，變更指令部3061B判定舊模型復活條件是否成立。變更指令部3061B在舊模型復活條件成立的情況下，進入步驟S410，在舊模型復活條件不成立的情況下，結束此次流程圖的處理。
藉由步驟S410，變更指令部3061B將舊模型復活指令輸出至正常模型記憶部304。具體而言，變更指令部3061B廢棄(消除)正常模型記憶部304的現在的正常模型，並且使更新之前的正常模型返回被診斷處理部305使用的正常模型的位址。
變更指令部3061在步驟S410的處理完成後，結束此次流程圖的處理。
如此，監視裝置30能夠在能夠判斷現在使用的正常模型與機器10的正常的運轉狀態之間的偏離超過規定基準的模型更新條件成立後，使用事後取得的批次資料，對正常模型進行更新。因此，監視裝置30能夠根據機器10的正常的範圍的變化，對正常模型進行更新。因此，監視裝置30能夠根據機器10的運轉狀態的正常的範圍的變化，恰當地進行與機器10的運轉狀態的異常相關的診斷。
另外，監視裝置30可以在能夠判斷更新後的正常模型與機器10的正常的運轉狀態之間的偏離超過規定基準的模型復活條件成立後，使被診斷處理部305使用的正常模型返回更新前的正常模型。因此，監視裝置30可以在雖然進行了正常模型的更新但是更新後的正常模型不適合機器10的正常的運轉狀態的狀況下，使被診斷處理部305使用的正常模型返回更新前的正常模型。由此，監視裝置30能夠更恰當地進行與機器10的運轉狀態的異常相關的診斷。
[作用]
接下來，對本實施方式的監視裝置30的作用進行說明。
在本實施方式中，監視裝置30包括模型生成部3034和診斷部3054。具體而言，模型生成部3034基於事先取得的、表示機器10或設備(以下，稱為“機器10等”)的每規定期間的時間序列的運轉狀態的運轉資料(例如，每個批次過程的批次資料)，生成表示機器10等的正常的運轉狀態的正常模型。另外，診斷部3054基於正常模型、以及事後取得的機器10等的規定期間的運轉資料，進行與機器10等的運轉狀態的異常相關的診斷。並且，模型生成部3034對被診斷部3054使用的正常模型自動進行更新。
由此，監視裝置30能夠在例如機器10等的運轉狀態的正常的範圍變化的情況下，根據其變化，對正常模型進行更新。因此，監視裝置30能夠根據機器10等的運轉狀態的正常的範圍的變化，恰當地進行與機器10等的異常相關的診斷。
另外，在本實施方式中，模型生成部3034可以根據機器10等的運轉狀態的正常的範圍的經時變化，對被診斷部3054使用的正常模型自動進行更新。
由此，監視裝置30能夠根據機器10等的運轉狀態的正常的範圍的經時變化，恰當地進行與機器10等的異常相關的診斷。
另外，在本實施方式中，模型生成部3034可以在模型更新條件成立後，對被診斷部3054使用的正常模型自動進行更新。
由此，監視裝置30藉由適當設定表示機器10等的運轉狀態的正常的範圍的變化的模型更新條件，能夠根據其成立與否，對正常模型進行更新。
另外，在本實施方式中，模型更新條件可以是在由診斷部3054診斷為正常的範圍內，相對於正常模型，機器10等的規定期間的運轉資料相對較大偏離。另外，模型更新條件可以是將被診斷部3054使用的正常模型的使用開始時、或被模型生成部3034用於正常模型的生成的每個規定期間的運轉資料的取得結束時作為基準，被機器10等生產的物品的生產數量PN超過模型更新基準PNth。另外，模型更新條件可以是將被診斷部3054使用的正常模型的使用開始時、或被模型生成部3034用於正常模型的生成的每個規定期間的運轉資料的取得結束時作為基準，經過時間Tm超過模型更新基準Tm_th。另外，模型更新條件可以是將被診斷部3054使用的正常模型的使用開始時、或被模型生成部3034用於正常模型的生成的每個規定期間的運轉資料的取得結束時作為基準，在機器10等的周邊的環境條件產生了相對較大的變化。
由此，監視裝置30可以將表示機器10等的運轉狀態的正常的範圍的變化的模型更新條件規定為各種各樣。因此，監視裝置30能夠提高正常模型的自動更新的定時的自由度。
另外，在本實施方式中，監視裝置30可以包括第一輸入部(例如，輸入裝置36、通訊連接端子35)以及設定部3062A。具體而言，第一輸入部可以接受來自用戶的輸入。並且，設定部3062A可以根據由第一輸入部接受的規定的輸入來進行與模型更新條件相關的設定。
由此，監視裝置30能夠使使用者決定(設定)正常模型的自動更新的定時。
另外，在本實施方式中，模型生成部3034可以基於用於藉由診斷部3054進行的診斷的每個規定期間的運轉資料，對被診斷部3054使用的正常模型自動進行更新。
由此，監視裝置30可以基於診斷部3054的診斷結果，選擇使用相當於正常的運轉狀態的每個規定期間的運轉資料，恰當地對正常模型進行更新。
另外，在本實施方式中，模型生成部3034可以基於將用於生成更新前的正常模型而使用的複數個規定期間的每一個期間的運轉資料中的一定數量或一定比率置換為由診斷部3054在基於更新前的正常模型的診斷中使用的每個規定期間的運轉資料的複數個規定期間的每一個期間的運轉資料，對被診斷部3054使用的正常模型自動進行更新。
由此，具體而言，監視裝置30能夠使最近的機器10的運轉狀態的正常的範圍反映於正常模型。
另外，在本實施方式中，模型生成部3034可以藉由診斷部3054基於在基於更新前的正常模型的診斷中使用的、最近的規定數量(例如，規定數量BN)的每個規定期間的運轉資料，對被診斷部3054使用的正常模型自動進行更新。
由此，具體而言，監視裝置30能夠使最近的機器10的運轉狀態的正常的範圍反映於正常模型。
另外，在本實施方式中，監視裝置30可以包括第二輸入部(例如，輸入裝置36、通訊連接端子35)、以及設定部3062B。具體而言，第二輸入部可以接受來自用戶的輸入。並且，設定部3062B根據由第二輸入部接受的規定的輸入，進行與模型生成部3034基於在藉由診斷部3054進行的診斷中使用的每個規定期間的運轉資料對由診斷部3054使用的正常模型自動進行更新的方相關的設定。
由此，監視裝置30能夠使使用者決定(設定)正常模型的更新方法。
另外，在本實施方式中，診斷部3054可以在藉由模型生成部3034進行更新後的正常模型相對於機器10等的運轉狀態的正常的範圍偏離超過規定基準的情況下，使在診斷中使用的正常模型返回更新前的正常模型。
由此，監視裝置30即在更新後的正常模型無法恰當表達機器10等的運轉狀態的正常的範圍的情況下，亦能夠藉由返回更新前的正常模型，持續恰當地進行與機器10等的異常相關的診斷。
另外，在本實施方式中，診斷部3054可以在能夠判斷藉由模型生成部3034進行更新後的正常模型相對於機器10等的運轉狀態的正常的範圍偏離超過規定基準的舊模型復活條件成立的情況下，使在診斷中使用的正常模型返回更新前的正常模型。並且，舊模型復活條件可以是在正常模型的更新的前後，藉由診斷部3054診斷為機器10等的運轉狀態存在異常的頻率超過規定基準而增加。另外，舊模型復活條件可以是在正常模型的更新的前後，基於診斷部3054的診斷結果表現出向自機器10等的運轉狀態的正常的範圍偏離的方向的超過了規定基準的變化。
由此，監視裝置30在更新後的正常模型無法恰當表達機器10等的運轉狀態的正常的範圍的情況下，具體而言，能夠返回更新前的正常模型。
另外，在本實施方式中，監視裝置30可以包括第三輸入部(例如，輸入裝置36、通訊連接端子35等)以及設定部3062C。具體而言，第三輸入部可以接受來自用戶的輸入。並且，設定部3062C可以根據由第三輸入部接受的規定的輸入，進行與舊模型復活條件相關的設定。
由此，監視裝置30能夠使使用者決定(設定)使正常模型返回更新前的狀態的定時。
以上，雖然對實施方式進行了詳述，但是本發明不限於特定的實施方式，在申請專利範圍記載的主旨的範圍內，能夠進行各種變形/變更。
Embodiments will be described below with reference to the drawings. [Overview of Monitoring System] With reference to FIG. 1 , an overview of a monitoring system 1 according to the present embodiment will be described. FIG. 1 is a diagram showing an example of a monitoring system 1 . The monitoring system 1 performs monitoring related to the operating state of the equipment 10 . The monitoring system 1 includes a machine 10 , a control device 20 , a monitoring device 30 , and a terminal device 40 . The machine 10 is processed by a batch process (batch-batch processing). The machine 10 is, for example, a machine installed in a factory or the like and used in a production process. For example, the machine 10 includes a paper feeding machine installed in a paper factory, a printing factory, and the like, a rotary shearer (shearing machine), a punching machine, and the like installed in a metal stamping factory and the like. The number of devices 10 included in the monitoring system 1 may be one or plural. In addition to the machine 10, the monitoring system 1 can monitor the operation status of a plurality of machines installed in a factory such as a production line as a whole and perform a batch process (production machine group) as an object. The control device 20 controls the operation of the machine 10 . Specifically, the control device 20 can obtain various measurement data indicating the operating state of the machine 10 from the machine 10, and based on the acquired measurement data, control the operation conditions of the machine 10 in a manner that conforms to predetermined operating conditions (for example, a predetermined sequence). Actions are controlled. The control device 20 is communicably connected to the machine 10 via, for example, a one-to-one communication line or a communication line such as a field network in a factory. The control device 20 is, for example, a PLC (Programmable Logic Controller), an edge processor, or the like. One control device 20 may be provided for each control target machine 10, or one may be provided for a plurality of control target machines. That is, the number of control devices 20 included in the monitoring system 1 may be one, and when a plurality of devices 10 are included in the monitoring system 1 , there may be a plurality of them. The monitoring device 30 (an example of a diagnostic device) monitors the operating state of the equipment 10 . Specifically, the monitoring device 30 can perform a diagnosis related to an abnormality in the operating state of the machine 10 . The diagnosis related to the abnormality of the operating state of the equipment 10 includes, for example, the diagnosis of the presence or absence of an abnormality in the operating state of the equipment 10 . In addition, the diagnosis related to the abnormality of the operating state of the equipment 10 includes, for example, a diagnosis according to the degree of abnormality in the operating state of the equipment (hereinafter referred to as “abnormal degree”). One monitoring device 30 may be provided for each device 10 to be monitored, or one may be provided for a plurality of devices 10 to be monitored. That is, one monitoring device 30 included in the monitoring system 1 may be used, and when a plurality of devices 10 are included in the monitoring system 1, a plurality of them may be used. The monitoring device 30 obtains various data (hereinafter referred to as "operation data") indicating the operating state of the machine 10 from the control device 20 for each predetermined sampling period through a predetermined communication line, and by monitoring the obtained operation data, Diagnosis related to the abnormality of the machine 10 is performed. The sampling period is specified in the range of several hundred milliseconds to tens of seconds, for example. The operation data includes, for example, various measurement data obtained from the machine 10 by the control device 20 and indicating the operating state of the machine 10 . In addition, the operation data includes, for example, control data such as control commands generated by the control device 20 for the control of the machine 10 . The operation data includes, for example, data of a plurality of types of state variables (hereinafter referred to as “process variables”) of the machine 10 indicating temperature, pressure, torque, flow rate, etc. of a predetermined part of the machine 10 . Therefore, the operation data is expressed, for example, as vector data representing a plurality of types of states (process variables). The predetermined communication lines include, for example, one-to-one communication lines. In addition, the predetermined communication line includes, for example, a local area network (LAN: Local Area Network) such as an on-site network installed in a facility such as a factory where the device 10 and the control device 20 are installed. In addition, the predetermined communication line includes, for example, a wide area network (WAN: Wide Area Network). The wide area network includes, for example, a mobile communication network terminated by a base station, a satellite communication network using communication satellites, and the Internet. In addition, the predetermined communication line includes, for example, a short-distance communication line using a predetermined wireless communication method. The short-distance communication line includes, for example, wireless communication lines based on communication methods such as WiFi and Bluetooth (registered trademark). The monitoring device 30 is, for example, a terminal device installed in the same facility or site as a facility such as a factory where the machine 10 and the control device 20 are installed. The terminal device is, for example, a stationary terminal device such as a PLC or a desktop PC (Personal Computer). In addition, the terminal device may be, for example, a portable terminal device (portable terminal) such as a smartphone, a tablet terminal, or a laptop PC. In addition, the monitoring device 30 is, for example, a server device. The server device is, for example, a local deployment server or a cloud server installed outside the site of a facility such as a factory where the machine 10 and the control device 20 are installed. In addition, the server device is, for example, the machine 10 and the control device 20 may be an edge server installed in or adjacent to the facility site such as a factory. It should be noted that the monitoring device 30 can directly obtain the operation data from the machine 10 . The terminal device 40 is communicably connected to the monitoring device 30 via a predetermined communication line, and is a user terminal that provides information on the monitoring result of the monitoring device 30 to the user. The terminal device 40 may be a stationary terminal device such as a desktop PC, or may be a portable terminal such as a smart phone, a tablet computer terminal, or a laptop PC. [Hardware Configuration of Monitoring Device] Next, the hardware configuration of the monitoring device 30 according to the present embodiment will be described with reference to FIG. 2 . FIG. 2 is a diagram showing an example of a hardware configuration of the monitoring device 30 . The function of the monitoring device 30 is realized by arbitrary hardware or a combination of arbitrary hardware and software. For example, as shown in Figure 2, the monitoring device 30 includes an external terminal 31 connected by a bus bar B, an auxiliary memory device 32, a memory device 33, a CPU (Central Processing Unit) 34, a communication connection terminal 35, an input device 36, and a display device 37 . The external terminal 31 functions as a terminal for reading data from the recording medium 31A and writing data to the recording medium 31A. The recording medium 31A includes, for example, a floppy disk, a CD (Compact Disc), a DVD (Digital Versatile Disc), a BD (Blu-ray (registered trademark) Disc), an SD memory card, a USB (Universal Serial Bus) memory, and the like. In this way, the monitoring device 30 reads various data used for processing through the recording medium 31A and stores them in the auxiliary storage device 32 , so that programs for realizing various functions can be installed. It should be noted that the monitoring device 30 can obtain various data and programs from an external device through the communication connection terminal 35 . The auxiliary memory device 32 is used to store various installed programs, and to store files and data required for various processes. The auxiliary memory device 32 includes, for example, HDD (Hard Disc Drive), SSD (Solid State Drive), and the like. The memory device 33 reads and stores the program from the auxiliary memory device 32 when there is an activation command of the program. The memory device 33 includes, for example, DRAM (Dynamic Random Access Memory) and SRAM (Static Random Access Memory). The CPU 34 executes various programs loaded into the memory device 33 from the auxiliary memory device 32 , and implements various functions related to the monitoring device 30 according to the programs. The communication connection terminal 35 is used as a terminal for communicably connecting with an external device. Thus, the monitoring device 30 is communicably connected to external devices such as the control device 20 and the terminal device 40 via the communication connection terminal 35 . In addition, the communication connection terminal 35 may have a plurality of types of communication connection terminals according to a communication method with a connected device or the like. The input device 36 accepts various inputs from the user. The input device 36 includes, for example, an operation input device that accepts a mechanical operation input from a user. The operation input device includes, for example, a button, a toggle switch, a pull rod, and the like. In addition, the operation input device includes, for example, a touch screen mounted on the display device 37 , a touch panel provided separately from the display device 37 , and the like. In addition, the input device 36 includes, for example, a voice input device capable of receiving voice input from a user. The voice input device includes, for example, a microphone capable of collecting a user's voice. In addition, the input device 36 includes, for example, a gesture input device capable of accepting gesture input from a user. The gesture input device includes, for example, a camera capable of capturing images of user gestures. In addition, the input device 36 includes, for example, a living body input device capable of receiving a living body input from a user. The living body input device includes, for example, a camera capable of acquiring image data including information on the user's fingerprint and iris. The display device 37 displays information screens and operation screens to the user under the control of the monitoring device 30 . The display device 37 includes, for example, a liquid crystal display, an organic EL (Electroluminescence) display, and the like. [Functional Configuration of Monitoring Device] Next, the functional configuration of the monitoring device 30 according to the present embodiment will be described with reference to FIGS. 3 to 10 . FIG. 3 is a functional block diagram showing an example of the functional configuration of the monitoring device 30 . Fig. 4 is a diagram showing an example of operation data for each batch process. Fig. 5 is a schematic diagram showing an example of a lot data. FIG. 6 is a schematic diagram showing an example of a data conversion method. FIGS. 7 to 9 are diagrams showing first to third examples of changes in the normal state of the operation data of the machine 10 during the batch process. FIG. 10 is a diagram for explaining an example of update conditions of a normal model. As shown in FIG. 3 , the monitoring device 30 includes a batch data storage unit 301 , a data storage unit 302 , a model generation processing unit 303 , a normal model storage unit 304 , a diagnosis processing unit 305 , and a model change processing unit 306 . The functions of the batch data storage unit 301 and the normal model storage unit 304 are realized, for example, by a storage area defined in the auxiliary storage device 32 . In addition, the functions of the data storage unit 302, the model generation processing unit 303, the diagnosis processing unit 305, and the model change processing unit 306 are performed on the CPU 34 by loading the program installed in the auxiliary memory device 32 into the memory device 33, for example. be executed to achieve. The batch data storage unit 301 stores time-series operation data (hereinafter referred to as “batch data”) received from the control device 20 for each batch process of the machine 10 . For example, as shown in FIG. 4 , the operation data includes, for example, state data for each process variable such as temperature, pressure, torque, flow rate, and for each sampling cycle. And, in the batch process, the state data of the same type (variable) shows a similar waveform (graph) for each batch. For example, as shown in FIG. 5 , the batch profile is represented as a three-dimensional profile for each batch i, for the elapsed time k in each batch i from the start of the batch, and for each process variable j. Batch i represents an integer greater than 1 and the number of stored batches I is less than, time k represents an integer greater than 1 and the number of samples in a batch is less than K, and process variable j represents an integer greater than 1 but less than the number of types of process variables J integer. Hereinafter, the batch data may be expressed as x(i, j, k) using the batch process i, time k, and process variable j. Returning to FIG. 3 , the data storage unit 302 stores the batch data in the batch data storage unit 301 . Specifically, the data storage unit 302 stores, in the batch data storage unit 301 , the batch data when the operating state of the machine 10 is in the normal range. The model generation processing unit 303 performs processing for generating a normal model representing a normal operating state of the equipment 10 . The model generation processing unit 303 includes a data acquisition unit 3031 , a preprocessing unit 3032 , a data conversion unit 3033 , and a model generation unit 3034 . The data acquisition unit 3031 acquires, from the lot data storage unit 301 , the lot data in which the operating state of the machine 10 corresponds to a normal state, as basic data for generating a normal model. The preprocessing unit 3032 performs predetermined preprocessing on the batch data x(i, j, k) acquired by the data acquiring unit 3031, and outputs the preprocessed batch data x s (i, j, k). The preprocessing unit 3032 performs standardization processing on the batch data acquired by the data acquisition unit 3031, for example. Specifically, the preprocessing unit 3032 can use the average μj,k and the standard deviation σj,k among a plurality of batches i of the batch data x(i,j,k) acquired by the data acquisition unit 3031, Standardize the batch data x(i,j,k). The data conversion unit 3033 converts the batch data x s (i, j, k) in the three-dimensional form and preprocessed by the preprocessing unit 3032 into two-dimensional batch data X s (j, k). For example, as shown in FIG. 6 , the data conversion unit 3033 decomposes the batch data x s (i, j, k) into batch data groups x s (1, j, k) of the batch number I for each batch i ), x s (2,j,k),･･･, x s (I,j,k). Furthermore, the data converting unit 3033 generates batch data X s (j,k) corresponding to matrix data of J rows I･K columns by combining the decomposed batch data groups in the time k axis direction. Returning to FIG. 3 , the model generation unit 3034 (an example of the generation unit) performs machine learning based on the batch data x s (i, j, k) obtained by the data acquisition unit 3031 and representing the normal state of the machine 10, and generates A normal model representing the normal state of the machine 10 . The model generation unit 3034 generates, for example, a loading matrix (Loading Matrix) obtained by principal component analysis (PCA: Principal Component Analysis) as a normal model. It should be noted that, when the monitoring system 1 includes a plurality of machines 10 , a normal model is generated for each of the machines 10 . In addition, the model generation unit 3034 may generate a normal model by any method. For example, the model generating unit 3034 may generate a normal model using independent component analysis (ICA: Independent Component Analysis) instead of principal component analysis. In addition, for example, the model generating unit 3034 may generate a normal model by applying a support vector machine (SVM: Support Vector Machine), a deep neural network (DNN: Deep Neural Network), or the like. The normal model generated by the model generation unit 3034 is stored in the normal model storage unit 304 . In addition, as will be described later, when the normal model is updated by the model generation unit 3034, the normal model after the update is stored in the normal model storage unit 304, and the normal model before the update is also stored. Specifically, in the normal model storage unit 304, the area (address) of the normal model used for the diagnosis related to the abnormality of the operating state of the device 10 by the diagnostic processing unit 305 and the normal model before the memory update are stored. The area (address) of the model is distinguished. The diagnostic processing unit 305 performs processing for diagnosing abnormalities in the operating state of the equipment 10 . The diagnosis processing unit 305 includes a data acquisition unit 3051 , a preprocessing unit 3052 , an index value calculation unit 3053 , a diagnosis unit 3054 , and a notification unit 3055 . The data acquisition unit 3051 acquires the operation data of the machine 10 to be diagnosed imported from the control device 20 . The preprocessing unit 3052 performs the same preprocessing as that of the preprocessing unit 3032 on the operation data acquired by the data acquisition unit 3051 . The index calculation unit 3053 calculates the operating state of the machine 10 based on the operation data of the machine 10 that has been preprocessed by the preprocessing unit 3052 and the latest normal model stored in the normal model storage unit 304 . The predetermined index value of the diagnosis related to the abnormality is calculated. The index value calculation unit 3053 calculates Q statistic and T statistic as predetermined index values based on, for example, the operation data of the preprocessed equipment 10 and the load matrix as a normal model. In addition, the index value calculation unit 3053 may calculate respective function values of the Q statistic and the T statistic based on the entire batch process of the machine 10 from the start of the current batch process as predetermined index values, for example. The diagnosis unit 3054 performs a diagnosis related to an abnormality in the operating state of the equipment 10 based on the index value calculated by the index value calculation unit 3053 . The diagnosis unit 3054 diagnoses that there is a sign of abnormality in the operating state of the equipment 10, for example, when at least one of the Q statistic and the T 2 statistic that are index values exceeds a predetermined standard (hereinafter, "abnormal symptom standard") IVth1. . In addition, the diagnosis unit 3054 diagnoses that, for example, when at least one of the Q statistic and the T 2 statistic, which are index values, exceeds a predetermined standard (hereinafter referred to as "abnormality occurrence standard") IVth2 greater than the abnormality sign standard IVth1 There is an abnormality in the operating state of the machine 10 . In this case, the Q statistic and the T 2 statistic may be the same or different for the abnormality sign criterion IVth1 and the abnormality occurrence criterion IVth2. In addition, the diagnostic unit 3054 can diagnose the degree of abnormality of the operating state of the equipment 10 such that the greater the Q statistic and the T 2 statistic as index values, the higher the abnormality of the operating state of the equipment 10 . In addition, the diagnosis unit 3054, for example, when at least one of the function value based on the Q statistic and the function value based on the T 2 statistic of the current entire batch process as an index value exceeds the abnormal symptom standard IVth1, diagnoses that There is a sign that the operating state of the machine 10d is abnormal. In addition, for example, when at least one of the function value based on the Q statistic and the function value based on the T 2 statistic of the current entire batch process as an index value exceeds the abnormality generation standard IVth2, it determines that the machine The operation status of 10 is abnormal. In this case, the abnormal symptom criterion IVth1 and the abnormality occurrence criterion IVth2 may be the same or different for the function value based on the Q statistic and the function value based on the T 2 statistic in the entire batch process. In addition, the diagnostic unit 3054 can diagnose the degree of abnormality of the operating state of the machine 10 in such a way that the larger the function value based on the Q statistic and the T 2 statistic of the entire batch process as an index value, the greater the machine 10 abnormality. The abnormality degree of the operation state of 10 is higher. The notification unit 3055 notifies the user of the diagnosis result by the diagnosis unit 3054 to the user. The notification unit 3055 notifies the user of the diagnosis result through, for example, the display device 37 . In addition, the notification unit 3055 can, for example, send the diagnosis result to the terminal device 40 through the communication connection terminal 35 , and notify the user of the diagnosis result by displaying the diagnosis result on the display of the terminal device 40 . The model change processing unit 306 performs processing for changing the normal model used in the diagnosis related to the abnormality of the operating state of the equipment 10 by the diagnosis processing unit 305 . The model change processing unit 306 includes a change instruction unit 3061 and a setting unit 3062 . The change command unit 3061 generates and outputs a command for changing the normal model used by the diagnostic processing unit 305 . The change command unit 3061 includes change command units 3061A and 3061B. The change instruction unit 3061A generates a command for automatically updating the normal model used by the diagnosis processing unit 305 (hereinafter referred to as a “model update command” for convenience), and inputs it to the model generation processing unit 303 . For example, as shown in FIG. 7 , when the surrounding air temperature of a rotary shearer as the machine 10 is low, the torque is relatively larger than when the air temperature is high. This is because the viscosity of the lubricating grease used changes according to temperature. Therefore, when the air temperature around the rotary shear changes with the passage of time, the normal range of the torque changes. Also, for example, as shown in FIG. 8 , when the surrounding humidity of the paper feeding mechanism as the device 10 is high, the torque is relatively large compared to when the humidity is low. This is because the degree of moisture absorption of paper changes with changes in humidity, and as a result, the weight of paper to be conveyed changes. Therefore, when the humidity around the paper feeding machine changes with the passage of time, the normal range of the torque changes. In addition, for example, as shown in FIG. 9 , when the ambient air temperature of the press machine as the machine 10 is high, the torque is relatively larger than when the air temperature is low. This is because the mold expands or contracts as the temperature changes, and as a result, the sharpness of the mold changes. Therefore, when the air temperature around the press machine changes with the passage of time, the normal range of the torque changes. Therefore, the change command unit 3061A may output a model update command to the model generation processing unit 303 in accordance with changes in the normal range of the operating state of the equipment 10 to cause the model generation processing unit 303 to update the normal model. Specifically, if it can be determined that the normal range of the operating state of the machine 10 exceeds a predetermined condition (an example of the first condition) that deviates from the normal model currently in use (hereinafter referred to as "model update condition") If it is established, the change command unit 3061A can output a model update command. There may be one model update condition, or a plurality of them. When a plurality of model update conditions are specified, the change command unit 3061A may generate a model update command when any one of the plurality of model update conditions is satisfied. And output it to the model generation processing unit 303 . The model update condition is, for example, "the batch data of the machine 10 acquired after the event deviates relatively greatly from the current normal model". The batch data of the equipment 10 obtained after the event refers to the batch data of the equipment 10 acquired at a later timing than the batch data used to generate the normal model currently used by the diagnostic processing unit 305 . Specifically, the model update condition may be "the index value becomes relatively large". More specifically, for example, as shown in FIG. 10 , the model update condition may be "the index value exceeds a predetermined standard (hereinafter referred to as "model update standard") IVth3" (see the dotted line encircled in the figure). Also, the model update condition may be "the moving average of the index value exceeds the model update reference IVth3". In addition, the model update condition may be "the rate RT at which the index value exceeds the model update standard IVth3 exceeds a predetermined standard (hereinafter referred to as "model update standard") RTth in the current batch process." In addition, the model update condition may be, for example, "the number of consecutive times CN in which the index value exceeds the model update criterion IVth3 exceeds a predetermined criterion (hereinafter referred to as "model update criterion") CNth". For example, the model update criterion IVth3 may be specified in a range smaller than the abnormality sign criterion IVth1 and the abnormality occurrence criterion IVth2, or may be defined in a range between the abnormality sign criterion IVth1 and the abnormality occurrence criterion IVth2. In addition, as shown in FIG. 10 , the model update criterion IVth3 may be the same as the abnormal sign criterion IVth1. In addition, the model update condition may be, for example, "the production quantity PN of articles produced by the machine 10 exceeds a predetermined reference (hereinafter referred to as "model update reference") PNth from a predetermined starting point". Information related to the production quantity PN of items based on the machine 10 can be obtained from the control device 20 . In this case, the starting point of the production quantity PN may be, for example, the start of use of the currently used normal model, or the end of acquisition of lot data for generation of the currently used normal model. In addition, the model update condition includes, for example, "the elapsed time Tm from a predetermined starting point exceeds a predetermined reference (hereinafter referred to as "model update reference") Tm_th". The starting point of the elapsed time Tm may be, for example, the start of use of the currently used normal model, or the end of acquisition of batch data for generation of the currently used normal model. In addition, the model update condition includes, for example, "the change in the environmental condition of the installation place of the machine 10 exceeds a predetermined standard (hereinafter referred to as "model update standard"). Specifically, the model update condition may include "the temperature Tp around the machine 10 The amount of change ΔTp exceeds a predetermined reference (hereinafter referred to as "model update reference") ΔTp_th. In addition, the model update condition may include "the change amount ΔH of the humidity H around the device 10 exceeds a predetermined standard (hereinafter referred to as "model update standard") ΔHth. The temperature Tp, humidity H around the device 10, etc. The environmental conditions of the installation site are measured by, for example, the sensor 10 installed around the machine 10, and the information related to the environmental conditions of the installation site of the machine 10 is introduced into the monitoring device 30 through the control device 20. The surrounding area of the machine 10 The starting point of the change of the environmental conditions of the installation place of the machine 10 such as the change amount ΔTp of the temperature Tp, the change amount ΔH of the humidity H, etc. may be, for example, when the use of the normal model currently in use starts, or it may be the time for the current use. At the end of the acquisition of the batch data generated by the normal model. In addition, the command content related to the update method of the normal model can be included in the model update command. For example, as the update method of the model, there are multiple options, and there are multiple options Among them, the normal model can be updated by the update method set.The update method of the model can include, for example, using the batch data of the machine 10 of the latest specified number of BN (for example, 20) in the normal state to the normal model The method for updating. The batch data of the machine 10 in a normal state refers to the batch data in which the running state of the machine 10 is diagnosed as normal by the diagnosis unit 3054. Specifically, the model generation processing unit 303 uses For the batch data whose operation state is diagnosed as abnormal, the batch data of the nearest specified number BN of machines 10 in the normal state are traced back from the establishment of the model update condition, and a new normal model is generated. The model generation processing unit 303 can use the latest specified number of batch data to generate a new normal model for the end of each batch process on the premise that the operating state of the machine 10 is normal. The normal model used by the part 305 is updated. Specifically, by replacing the earliest batch data among the batch data of a predetermined number of BN used for the generation of the normal model last time, the latest batch data including the latest batch data can be used. New batch data of a specified number of BN updates the normal model. The model update condition in this case is "the batch process update of the machine 10". In addition, the model update method can include, for example, by using the currently used A certain amount or a certain ratio in the batch data generated by the normal model is replaced with the batch data obtained afterwards, and the machine 10 is in a normal state, thereby updating the normal model. The batch data obtained after the event refers to, in The batch data obtained after the time point when the batch data used for the generation of the normal model currently used is obtained. Specifically, the model generation processing part 303 uses A certain number or a certain ratio of batch data in the batch data is replaced with a new predetermined number BN of batch data obtained afterward in the normal operating state of the machine 10 to generate a new normal model. In this case, the newly added batch data (group) can be the latest batch data (group) selected retrospectively from the latest batch data, or it can be a batch selected based on some other condition data(group). The change command unit 3061B generates a command to discard the normal model currently used by the diagnostic processing unit 305 and return to the normal model before the most recent update (hereinafter referred to as an "old model resurrection command" for convenience), and outputs it to the normal model memory Section 304. Specifically, the change instruction unit 3061B deletes (discards) the latest normal model in the normal model storage unit 304 according to the old model revival command, and moves the latest normal model before the update to the normal model used by the diagnosis processing unit 305. address. Thus, the diagnostic processing unit 305 accesses the normal model before the update, and uses the normal model before the update to perform a diagnosis related to an abnormality in the operating state of the equipment 10 . For example, when the deviation between the currently used normal model and the normal range of the actual operating state of the machine 10 exceeds a predetermined standard (hereinafter referred to as "old model recovery standard"), the change instruction unit 3061B changes the normal model to the normal model. The storage unit 304 outputs an old model resurrection command. Specifically, if it can be judged that the deviation between the normal model currently in use and the normal range of the actual operating state of the machine 10 exceeds the prescribed condition of the old model revival standard (hereinafter referred to as "old model revival condition") ( An example of the second condition) is met, and the change instruction unit 3061B may output an old model resurrection instruction. The old model revival condition may be one or plural. If a plurality of old model revival conditions are specified, if any one of the plurality of old model revival conditions is satisfied, the change command unit 3061B may generate an old model revival command. , and output it to the normal model memory unit 304. The old model recovery condition is, for example, "the frequency Fq at which the operating state of the equipment 10 was diagnosed as abnormal by the diagnostic unit 3054 exceeds a predetermined reference (hereinafter referred to as "old model recovery standard") Fq_th before and after the latest normal model update". In addition, the old model revival condition may be, for example, "before and after the latest normal model update, based on the diagnosis result of the diagnosis unit 3054, the direction of deviation from the normal range of the operating state of the equipment 10 has exceeded a predetermined standard (hereinafter, called the "Old Model Resurrection Benchmark")". Specifically, the old model revival condition may be "the increment ΔIVm of the moving average IVm of the index value exceeds a predetermined standard (hereinafter referred to as "old model revival standard") ΔIVm_th before and after the latest normal model update". The setting unit 3062 performs setting related to the change (update or revival) of the normal model based on the input from the user. Input from the user is accepted, for example, by the input device 36 . In addition, the input from the user is performed through the terminal device 40, for example, by receiving a signal representing the user's input from the terminal device 40, through the communication connection terminal 35 (the first input part, the second input part, the third input part) An example of the Ministry) is accepted. The setting unit 3062 includes setting units 3062A to 3062C. The setting unit 3062A (an example of the first setting unit) performs setting related to the model update condition based on a predetermined input from the user. For example, the user can input settings related to model update conditions through a predetermined GUI (Graphical User Interface) displayed on the display device 37 or the display of the terminal device 40 . The setting unit 3062A sets the model update criteria IVth3, RTth, CNth, PNth, Tm_th, ΔTp_th, ΔHth, etc. based on predetermined input from the user, for example. The user may be able to directly set the model update criteria IVth3, RTth, CNth, PNth, Tm_th, ΔTp_th, ΔHth, etc. through the setting unit 3062A, or may be able to set it indirectly. Direct setting means that the user can designate values corresponding to the model update criteria IVth3, RTth, CNth, PNth, Tm_th, ΔTp_th, ΔHth, etc. by setting input. Indirect setting means that the user can designate variables in relational expressions for determining values corresponding to model update criteria IVth3, RTth, CNth, PNth, Tm_th, ΔTp_th, ΔHth, etc. by setting input. The setting unit 3062B (an example of the second setting unit) performs setting related to the update method of the normal model based on a predetermined input from the user. For example, the user can perform setting input related to the update method of the normal model through a predetermined GUI displayed on the display device 37 or the display of the terminal device 40 . For example, the setting unit 3062B can select and set an update method for one model from among a plurality of update methods for models based on a predetermined input from the user. In addition, the setting unit 3062B can set the number of batch data (predetermined number BN) used for updating the normal model, for example, based on a predetermined input from the user. In addition, the setting unit 3062B can set the number, ratio, and the like of batch data acquired afterward, among the batch data used for updating the normal model. The setting unit 3062C (an example of the third setting unit) performs setting related to the old model revival condition based on a predetermined input from the user. For example, the user can perform setting input related to the old model revival condition through a predetermined GUI displayed on the display device 37 or the display of the terminal device 40 . The setting unit 3062C sets the old model resurrection criteria Fq_th, ΔIVm_th, etc. based on predetermined input from the user, for example. The user may be able to directly set the old model resurrection criteria Fq_th, ΔIVm_th, etc. through the setting unit 3062C, or may be able to set it indirectly. [Normal Model Generation Processing] Next, the normal model generation processing by the monitoring device 30 (model generation processing unit 303 ) will be described with reference to FIG. 11 . FIG. 11 is a flowchart schematically showing an example of normal model generation processing by the model generation processing unit 303 . This flowchart is executed, for example, according to a predetermined input (request) from the user. In addition, this flowchart is executed after a model update command is output from the model change processing unit 306 (change command unit 3061A), for example. As shown in FIG. 11 , in step S102 , the data acquisition unit 3031 acquires batch data (training data) corresponding to the normal state of the machine 10 for generating a normal model from the batch data storage unit 301 . The model generation processing unit 303 proceeds to step S104 after the processing of step S102 is completed. Through step S104, the preprocessing unit 3032 performs predetermined preprocessing on the batch data acquired in step S102. The model creation processing unit 303 proceeds to step S106 after the processing of step S104 is completed. Through step S106, the data converting unit 3033 converts the batch data preprocessed in step S104 into batch data in two-dimensional form. The model creation processing unit 303 proceeds to step S108 after the processing of step S106 is completed. In step S108, the model generating unit 3034 generates a normal model based on the batch data converted into two-dimensional form in step S106. As described above, the generated normal model is stored in the normal model storage unit 304 . After the processing of step S108 is completed, the model generation processing unit 303 ends the processing of the flowchart of this time. In this way, the monitoring device 30 can generate a normal model based on the lot data corresponding to the normal operating state of the machine 10 . In addition, the monitoring device 30 may update the normal model according to the model update command when the normal model used by the diagnostic processing unit 305 needs to be updated. [Diagnostic processing related to abnormality in operating state of equipment] Next, diagnostic processing related to abnormal operating state of equipment 10 performed by monitoring device 30 (diagnostic processing unit 305 ) will be described with reference to FIG. 12 . FIG. 12 is a flowchart schematically showing an example of diagnostic processing related to an abnormality in the operating state of the equipment 10 performed by the diagnostic processing unit 305 . This flowchart is repeated for each prescribed processing cycle, for example between the start and the end of the batch process of the machine 10 . The start and end of the batch process are grasped by, for example, signals sent from the control device 20 and received by the monitoring device 30 indicating the start and end of the batch process of the machine 10 . In this example, a flag F indicating the presence or absence of an abnormality in the operating state of the equipment 10 is used. The flag F is initialized at the beginning of the batch process of the machine 10 to "0" indicating a state where there is no abnormality. As shown in FIG. 12 , in step S202 , the data acquisition unit 3051 acquires the latest operating data of the machine 10 imported into the monitoring device 30 . The diagnostic processing unit 305 proceeds to step S204 after completing the processing of step S202. In step S204, the preprocessing unit 3052 performs predetermined preprocessing on the operation data acquired in step S202. The diagnostic processing unit 305 proceeds to step S206 after the processing of step S204 is completed. In step S206, the index value calculation unit 3053 calculates an index value based on the latest operating data preprocessed in step S204 and the normal model. The diagnostic processing unit 305 proceeds to step S208 after the processing of step S206 is completed. Through step S208, the diagnosis unit 3054 performs a diagnosis related to the operating state of the equipment 10 based on the index value calculated at step S206. The diagnostic processing unit 305 proceeds to step S210 after the processing of step S208 is completed. Through step S210, the diagnosis unit 3054 determines whether there is any abnormality in the diagnosis result of step S208. The diagnosis unit 3054 proceeds to step S212 if there is an abnormality in the diagnosis result, and proceeds to step S216 if there is no abnormality. Through step S212, the notification unit 3055 notifies the user of the diagnosis result indicating that the operating state of the machine 10 is abnormal. The content of the notification regarding the diagnosis result may be, for example, only the fact that there is an abnormality in the operating state of the equipment 10 , or may include information serving as a basis for the fact (diagnosis result) in addition to the fact. The information used as the basis for the diagnosis result includes, for example, information such as a graph showing time-series changes in index values. Hereinafter, the same may be applied to the content of the notification in step S216 described later. The diagnostic processing unit 305 proceeds to step S214 after the processing of step S212 is completed. In step S214 , the diagnosis processing unit 305 sets the flag F to “1” (F=1) indicating that there is an abnormality in the operating state of the device 10 . Thus, the monitoring device 30 (data storage unit 302 described later) can determine whether the batch data of a specific batch process indicates a normal state of the machine 10 by checking the flag F (see FIG. 13 ). After the processing of step S214 is completed, the diagnosis processing unit 305 ends the processing of the flowchart of this time. On the other hand, through step S216 , the notification unit 3055 notifies the user of the diagnosis result related to the operating state of the machine 10 . Specifically, 3055 notifies the user of the diagnosis result indicating that there is no abnormality in the operation state of the equipment 10 or that there is a sign of abnormality. After the processing of step S216 is completed, the diagnosis processing unit 305 ends the processing of the flowchart of this time. In this way, the monitoring device 30 can diagnose the operating state of the machine 10 online using the normal model representing the normal operating state of the machine 10 and notify the user of the diagnosis result. [Batch Data Storage Processing] Next, referring to FIG. 13 , the storage processing of batch data showing the normal operating state of the equipment 10 by the monitoring device 30 (data storage unit 302 ) will be described. FIG. 13 is a flowchart schematically showing an example of batch data storage processing by the data storage unit 302 . This flowchart is executed, for example, after the batch process of the machine 10 has ended. As shown in FIG. 13 , the data storage unit 302 determines whether or not the flag F is “0” indicating that there is no abnormality in the operation state of the equipment 10 . The data storage unit 302 enters step S304 when the flag F is "0", and ends the process when the flag F is not "0", that is, "1" indicating that the operating state of the machine 10 is abnormal. Figure processing. By step S304, the data storage unit 302 saves (buffers) in the memory device 33, etc., the time-series operation data from the beginning to the end of the current batch process as batch data in the batch data storage unit. 301 in. After the data storage unit 302 completes the processing of step S304, it ends the processing of the flowchart this time. In this way, the monitoring device 30 can memorize only the lot data of the machines 10 imported from the control device 20 , which are diagnosed by the diagnostic processing unit 305 as having no abnormality in the operating state. Therefore, the monitoring device 30 can update the normal model using the batch data stored after the acquisition of the batch data used for the generation of the normal model currently used is completed. [Normal Model Changing Process] Next, the changing process of the normal model used by the diagnostic processing unit 305 by the monitoring device 30 (model changing processing unit 306 ) will be described with reference to FIG. 14 . FIG. 14 is a flowchart schematically showing an example of normal model change processing by the model change processing unit 306 (change instruction unit 3061 ). This flowchart is executed, for example, after the batch process of the machine 10 has ended. As shown in FIG. 14 , the change instruction unit 3061 obtains the latest data for determining whether the currently used normal model needs to be changed, that is, whether the model update condition and/or the old model revival condition are satisfied. The change command unit 3061 proceeds to step S404 after the processing of step S402 is completed. In step S404, the change command unit 3061A determines whether the model update condition is satisfied. The change command unit 3061 proceeds to step S406 when the model update condition is satisfied, and proceeds to step S408 when the model update condition is not satisfied. In step S406 , the change instruction unit 3061A sends a model update command to the model generation processing unit 303 , and the model generation processing unit 303 updates the normal model used by the diagnosis processing unit 305 . After the processing of step S406 is completed, the change command unit 3061 ends this processing. On the other hand, in step S408, the change command unit 3061B determines whether or not the old model revival condition is satisfied. The change command unit 3061B proceeds to step S410 when the old model restoration condition is satisfied, and ends the processing of the flowchart this time when the old model restoration condition is not satisfied. Through step S410 , the change command unit 3061B outputs the old model revival command to the normal model storage unit 304 . Specifically, the change command unit 3061B discards (deletes) the current normal model in the normal model storage unit 304 , and returns the normal model before updating to the address of the normal model used by the diagnostic processing unit 305 . After the processing of step S410 is completed, the change command unit 3061 ends the processing of the flowchart of this time. In this way, the monitoring device 30 can update the normal model using the batch data acquired afterwards after the model update condition that can determine that the deviation between the normal model currently used and the normal operating state of the machine 10 exceeds a predetermined reference is satisfied. Therefore, the monitoring device 30 can update the normal model according to the change in the normal range of the machine 10 . Therefore, the monitoring device 30 can appropriately diagnose abnormalities in the operating state of the machine 10 based on changes in the normal range of the operating state of the machine 10 . In addition, the monitoring device 30 may return the normal model used by the diagnostic processing unit 305 to the one before the update after the model restoration condition that can determine that the deviation between the updated normal model and the normal operating state of the machine 10 exceeds a predetermined reference is satisfied. normal model. Therefore, the monitoring device 30 can return the normal model used by the diagnostic processing unit 305 to the normal model before the update when the normal model is updated but the updated normal model is not suitable for the normal operating state of the machine 10 . Thereby, the monitoring device 30 can more appropriately perform a diagnosis related to an abnormality in the operating state of the equipment 10 . [Operation] Next, the operation of the monitoring device 30 of this embodiment will be described. In this embodiment, the monitoring device 30 includes a model generation unit 3034 and a diagnosis unit 3054 . Specifically, the model generating unit 3034 is based on previously acquired operation data (for example, each batch process batch data) to generate a normal model representing the normal operating state of the machine 10 and the like. In addition, the diagnosis unit 3054 performs a diagnosis related to an abnormality in the operating state of the equipment 10 or the like based on the normal model and the operation data of the equipment 10 or the like obtained afterward for a predetermined period. Furthermore, the model generation unit 3034 automatically updates the normal model used by the diagnosis unit 3054 . Thereby, the monitoring device 30 can update the normal model according to the change, for example, when the operating state of the equipment 10 etc. changes within a normal range. Therefore, the monitoring device 30 can appropriately diagnose abnormalities of the equipment 10 and the like based on changes in the operating state of the equipment 10 and the like within a normal range. In addition, in the present embodiment, the model generating unit 3034 may automatically update the normal model used by the diagnosed unit 3054 according to temporal changes in the normal range of the operating state of the equipment 10 and the like. As a result, the monitoring device 30 can appropriately diagnose abnormalities of the equipment 10 and the like based on changes over time in the normal range of the operating state of the equipment 10 and the like. In addition, in this embodiment, the model generation unit 3034 may automatically update the normal model used by the diagnosed unit 3054 after the model update condition is satisfied. In this way, the monitoring device 30 can update the normal model according to whether it is established or not by appropriately setting a model update condition indicating a change in the normal range of the operating state of the equipment 10 or the like. In addition, in the present embodiment, the model update condition may be within the range diagnosed as normal by the diagnosis unit 3054, and the operation data of the equipment 10 etc. for a predetermined period deviates relatively greatly from the normal model. In addition, the model update condition may be based on when the use of the normal model used by the diagnosis unit 3054 starts, or when the acquisition of the operation data for each predetermined period is completed by the model generation unit 3034 to generate the normal model. The production quantity PN of the produced items such as 10 exceeds the model update reference PNth. In addition, the model update condition may be based on the start of use of the normal model used by the diagnosis unit 3054 or the end of the acquisition of the operation data for each predetermined period used by the model generation unit 3034 to generate the normal model. Tm exceeds the model update reference Tm_th. In addition, the model update condition may be based on the start of the use of the normal model used by the diagnosis unit 3054 or the end of the acquisition of the operation data for each predetermined period used by the model generation unit 3034 to generate the normal model. The surrounding environmental conditions of Class 10 have undergone relatively large changes. Thus, the monitoring device 30 can define various model update conditions indicating changes in the normal range of the operating state of the equipment 10 and the like. Therefore, the monitoring device 30 can increase the degree of freedom in the timing of automatic update of the normal model. In addition, in this embodiment, the monitoring device 30 may include a first input unit (for example, the input device 36 and the communication connection terminal 35 ) and a setting unit 3062A. Specifically, the first input unit can accept input from a user. Furthermore, the setting unit 3062A may perform setting related to the model update condition based on a predetermined input accepted by the first input unit. Thereby, the monitoring device 30 enables the user to determine (set) the timing of automatic update of the normal model. In addition, in the present embodiment, the model generating unit 3034 may automatically update the normal model used by the diagnosing unit 3054 based on the operation data for each predetermined period used for diagnosis by the diagnosing unit 3054 . Accordingly, the monitoring device 30 can appropriately update the normal model by selecting and using the operation data for each predetermined period corresponding to the normal operation state based on the diagnosis result of the diagnosis unit 3054 . In addition, in the present embodiment, the model generation unit 3034 may replace a certain number or a certain ratio of the operating data for each of a plurality of predetermined periods used for generating the normal model before updating with the diagnostic unit 3054 The normal model used by the diagnostic unit 3054 is automatically updated with the operation data for each predetermined period of the operation data for each predetermined period used in the diagnosis based on the normal model before updating. In this way, specifically, the monitoring device 30 can reflect the normal range of the operating state of the nearest equipment 10 on the normal model. In addition, in the present embodiment, the model generation unit 3034 can use the diagnosis unit 3054 based on the last predetermined number (for example, a predetermined number BN) of operation for each predetermined period used in the diagnosis based on the normal model before updating. The data automatically updates the normal model used by the diagnostic unit 3054 . In this way, specifically, the monitoring device 30 can reflect the normal range of the operating state of the nearest equipment 10 on the normal model. In addition, in this embodiment, the monitoring device 30 may include a second input unit (for example, the input device 36 and the communication connection terminal 35 ), and a setting unit 3062B. Specifically, the second input unit can accept input from the user. Then, the setting unit 3062B performs the operation data used by the diagnosing unit 3054 on the basis of the operating data for each predetermined period used in the diagnosis performed by the diagnosing unit 3054 with the model generating unit 3034 based on the predetermined input received by the second input unit. The normal model automatically updates the party-related settings. Thus, the monitoring device 30 enables the user to determine (set) the method for updating the normal model. In addition, in this embodiment, the diagnosis unit 3054 may make the normal model updated by the model generation unit 3034 deviate from the normal range of the operating state of the machine 10 or the like by more than a predetermined standard, and use The normal model of returns the normal model before the update. Thus, even when the updated normal model cannot properly express the normal range of the operating state of the equipment 10, etc., the monitoring device 30 can continue to properly communicate with the equipment 10 and the like by returning to the normal model before the update. Anomaly-related diagnoses. In addition, in the present embodiment, the diagnosis unit 3054 may judge that the normal range of the normal model updated by the model generation unit 3034 deviates from the normal range of the operating state of the machine 10 and the like by exceeding a predetermined standard, and the old model revival condition is met. In this case, return the normal model used for diagnosis to the normal model before the update. In addition, the old model revival condition may be that the diagnosis unit 3054 diagnoses that the operating state of the equipment 10 and the like is abnormal and the frequency increases beyond a predetermined standard before and after the update of the normal model. Also, the old model revival condition may be a change exceeding a predetermined standard in a direction that deviates from the normal range of the operating state of the equipment 10 etc. based on the diagnosis result of the diagnosis unit 3054 before and after the update of the normal model. In this way, the monitoring device 30 can specifically return to the normal model before the update when the updated normal model cannot properly express the normal range of the operating state of the equipment 10 or the like. In addition, in this embodiment, the monitoring device 30 may include a third input unit (for example, the input device 36 , the communication connection terminal 35 , etc.) and a setting unit 3062C. Specifically, the third input unit can accept input from the user. Furthermore, the setting unit 3062C may perform setting related to the old model revival condition based on a predetermined input accepted by the third input unit. Thereby, the monitoring device 30 enables the user to determine (set) the timing at which the normal model is returned to the state before the update. As mentioned above, although embodiment was described in detail, this invention is not limited to a specific embodiment, Various deformation|transformation/changes are possible within the scope of the summary described in the claim.
