Described below are a method and device for recognizing a state of a noise-generating machine to be investigated, which emits structure-borne or air-borne sound.
Machines execute movements, during the course of which oscillation signals, in particular acoustic oscillation signals, are generated. The oscillation signals generated by a machine, a system or an electromechanical device allow conclusions to be drawn about its respective current state, which can change for example due to wear phenomena. For example a machine or device generates different oscillation signals after several years of use due to wear phenomena from those it generates immediately after production. As well as machines chemical systems can also produce noise signals, for example due to gas bubbles in containers or pipes. The ageing or wear of systems, machines and devices causes changes to the oscillation signals generated, in particular the acoustic emissions. The oscillation signals generated by a machine, system or device, in particular the acoustic noise signals, are not only a function of wear but also of the type of structure. Machines, systems and devices frequently feature different product types or models within a product group. For example a company can manufacture different variants of water or heating pumps, the generated noise signals of which differ. Also the objects manufactured within the product groups or product variants also have manufacturing tolerances, so that different products or objects can emit different noise signals due to manufacturing tolerances. In addition to ageing and loading influences as well as manufacturing tolerances the spatial properties of the environment in which the device or system is located also influence the noise signals or acoustic emissions emitted by the device or system.
With known methods for recognizing a state of a noise-generating machine to be investigated a plurality of recordings are made at a manufactured prototype, in order to generate a model of the respective noise-generating machine or system. A plurality of recordings, in particular sound recordings, which take into account different influencing variables, are made in a training process. For example acoustic noise signals emitted by a prototype are recorded in different weather conditions and with different loadings at different times. To take different influencing variables into account, a plurality of recordings must be made. Model generation based on the prototype becomes more complex, the more different variants of the product there are. The training data is used to generate a statistical model or a physical model of the object or product, which can then be used to classify a noise signal, which is emitted by a manufactured product during ongoing operation after commissioning. An operating state of a product can be monitored based on the classification of the noise signal, so that any error states occurring and thus the need for maintenance work can be recognized.
One disadvantage of a known procedure is that the noise signals or acoustic emissions emitted by a manufactured product can differ or deviate from the noise signals of the prototype even in a normal or error-free state. One reason for this may be that the manufactured product, which can be a machine, device or system for example, is in a different environment from that of the prototype when the training data was recorded. The useful signal, in other words the acoustic emissions of the object to be investigated, is therefore overlaid with ambient noise in the manner of an interference signal. For example the training data can be recorded at a prototype in a space with little reverberation, while the object to be investigated, perhaps a manufacturing machine, is located in a factory, where acoustic signals are reflected to a significant degree. The product or system to be investigated can also have a different acoustic emission spectrum from the prototype due to manufacturing tolerances or a different configuration.