Conventionally, there has been proposed an anomaly monitoring device for determining whether an apparatus is operating normally by using a classification function of a neural network (neuro-computer). For such an anomaly monitoring device, there exist various techniques in which an operation sounds or a vibration of an apparatus is converted to an electric signal by a sensor (transducer) to be used as a target signal, and an amount of characteristics with a plurality of parameters is extracted from the target signal to be classified by a neural network.
Various configurations of neural networks are known. For example, there has been proposed a competitive learning neural network (Self-Organizing Map (SOM)) which classifies a variety of amounts of characteristics into categories. The competitive learning neural network is a neural network having two layers, i.e., an input layer and an output layer, and having two modes of operation, a training mode and a checking mode.
In the training mode, training samples are given to the network, which is trained using an unsupervised learning scheme. If training samples are assigned with categories, neurons of an output layer can be associated with the categories clusters each including neurons of a like category can be formed. Therefore, in the training mode, a clustering map representing categories can be matched to clusters of neurons in the output layer.
In the checking mode, an amount of characteristics (input data) to be classified is given to the competitive learning neural network which completed the training process and the category of a cluster to which an excited neuron belong to is mapped with the clustering map so that the category of the input data can be classified (see, e.g., Japanese Patent Laid-open Application No. 2004-354111).
However, it takes long time to collect training samples corresponding to the anomaly of an apparatus since they are obtained only if the apparatus operates abnormally. Therefore, it has been proposed that only normal categories are created in the clustering map by using, as training samples, amounts of characteristics obtained from an apparatus which operates properly, and anomaly is detected when deviating from the normal categories.
However, an apparatus such as an air conditioner operates differently in summer and in winter. That is, there is a big change in target signals of summer and winter even if the apparatus operates normally. In such an apparatus, there are great differences in cluster locations depending on whether the apparatus has been trained in summer or winter. Therefore, if a clustering map produced in summer is used in winter for example, misjudgment is likely to occur.