FURNACE CONDITION LEARNING METHOD FOR BLAST FURNACE, FURNACE CONDITION LEARNING DEVICE, ABNORMALITY DETECTION METHOD, ABNORMALITY DETECTION DEVICE AND OPERATION METHOD

To provide a furnace condition learning method and a furnace condition learning device for a blast furnace, even in the case where the causal relation between the furnace condition abnormality to be detected and measured data is uncertain or in the case where the time to a reception of influence by the furnace condition abnormality to be detected in the measured data is uncertain, capable of creating a neural network capable of detecting the furnace condition abnormality of the blast furnace at high precision.SOLUTION: A furnace condition learning method for a blast furnace comprises a first step where the image data of a raceway part of a blast furnace imaged in an imaging period including a period in which the furnace condition abnormality of the blast furnace is generated are learned by an unsupervised type neural network, a second step where, regarding each neuron composing the unsupervised type neural network after the learning, the correlation coefficient between the ignition value of the neuron and an index showing the furnace condition abnormality is calculated and a third step where, based on the correlation coefficient, the neuron used for detecting the furnace condition abnormality is extracted as a neuron for abnormality detection.SELECTED DRAWING: Figure 5 【課題】検出したい炉況異常と測定したデータとの間の因果関係が不確かな場合や、測定したデータが検出したい炉況異常の影響を受けるまでの時間が不明である場合においても、高炉の炉況異常を精度よく検出可能なニューラルネットワークを生成可能な高炉の炉況学習方法及び炉況学習装置を提供すること。【解決手段】本発明に係る高炉の炉況学習方法は、高炉の炉況異常が発生した期間を含む撮影期間において撮影された高炉のレースウェイ部の画像データを無教師型のニューラルネットワークで学習する第１ステップと、学習後の無教師型のニューラルネットワークを構成する各ニューロンについて、ニューロンの発火値と炉況異常を示す指数との相関係数を算出する第２ステップと、相関係数に基づいて炉況異常を検出する際に用いるニューロンを異常検出用ニューロンとして抽出する第３ステップと、を含む。【選択図】図５