In-situ classification of faulty sounds is an important issue in machine health monitoring and diagnosis. However, in a noisy environment such as a factory, machine sound is always mixed up with environmental noises, and noise-only periods can exist when a machine is not in operation. Therefore, a deep neural network (DNN)-based fault classifier has to be able to distinguish noise from machine sound and be robust to mixed noises. To deal with these problems, we investigate on-site noise exposure (ONE) that exposes a DNN model to the noises recorded in the same environment where the machine operates. Like the outlier exposure technique, noise exposure trains a DNN classifier to produce a uniform predicted probability distribution against noise-only data. During inference, the DNN classifier trained by ONE outputs the maximum softmax probability as the noise score and determines the noise-only period. We mix machine sound and noises of the ToyADMOS2 dataset to simulate highly noisy data. A ResNet-based classifier trained by ONE is evaluated and compared with those trained by other out-of-distribution detection techniques. The test results show that exposing a model to on-site noises can make a model more robust than using other noises or detection techniques.