An apparatus for monitoring a switchgear, the apparatus comprising: - an input unit; - a processing unit; and - an output unit; wherein, the input unit is configured to provide the processing unit with a monitor infra-red image of a switchgear; wherein, the processing unit is configured to implement a machine learning classifier algorithm to analyse the monitor infrared image and determine if there is one or more anomalous hot spots in the switchgear; wherein, the machine learning classifier algorithm has been trained on the basis of a plurality of different training infra-red images, wherein the plurality of training infra-red images comprises a plurality of synthetic infra-red images generated from a corresponding plurality of visible images, wherein generation of the plurality of synthetic images comprises utilisation of an image processing algorithm configured to transform visible imagery to synthetic infra-red imagery, wherein generation of one or more synthetic infra-red images of the plurality of synthetic infra-red images comprises an addition of at least one hot spot to the one or more synthetic infrared images, and wherein the plurality of visible images comprises image data of a switchgear; and wherein, the output unit is configured to output information relating to the one or more anomalous hot spots. - an input unit; - a processing unit; and - an output unit; wherein, the input unit is configured to provide the processing unit with a monitor infra-red image of a switchgear; wherein, the processing unit is configured to implement a machine learning classifier algorithm to analyse the monitor infrared image and determine if there is one or more anomalous hot spots in the switchgear; wherein, the machine learning classifier algorithm has been trained on the basis of a plurality of different training infra-red images, wherein the plurality of training infra-red images comprises a plurality of synthetic infra-red images generated from a corresponding plurality of visible images, wherein generation of the plurality of synthetic images comprises utilisation of an image processing algorithm configured to transform visible imagery to synthetic infra-red imagery, wherein generation of one or more synthetic infra-red images of the plurality of synthetic infra-red images comprises an addition of at least one hot spot to the one or more synthetic infrared images, and wherein the plurality of visible images comprises image data of a switchgear; and wherein, the output unit is configured to output information relating to the one or more anomalous hot spots. wherein, the input unit is configured to provide the processing unit with a monitor infra-red image of a switchgear; wherein, the processing unit is configured to implement a machine learning classifier algorithm to analyse the monitor infrared image and determine if there is one or more anomalous hot spots in the switchgear; wherein, the machine learning classifier algorithm has been trained on the basis of a plurality of different training infra-red images, wherein the plurality of training infra-red images comprises a plurality of synthetic infra-red images generated from a corresponding plurality of visible images, wherein generation of the plurality of synthetic images comprises utilisation of an image processing algorithm configured to transform visible imagery to synthetic infra-red imagery, wherein generation of one or more synthetic infra-red images of the plurality of synthetic infra-red images comprises an addition of at least one hot spot to the one or more synthetic infrared images, and wherein the plurality of visible images comprises image data of a switchgear; and wherein, the output unit is configured to output information relating to the one or more anomalous hot spots. <EOS>