Acoustic signal is more and more frequently used to diagnose machines operated in industrial conditions where installation of sensors is hindered. Impact of background noise seems to be the major problem as part of analysis of such signal. In most cases of industrial environments, background level is high; thus, it prevents against concluding as per standard methods that have been used in diagnostic testing. This study specifies the problem related to diagnosing machines operated under variable loads. Synchronous methods are used for diagnosing these types of machines, those include synchronisation of diagnostic signal with revolutions of the diagnosed machine. For the purpose of this study an acoustic signal was used as the diagnostic signal. Application of the synchronous method (order analysis) enables eliminating an impact of background noise derived from other sources. This study specifies application of acoustic signal to diagnose planetary gear in laboratory testing rig in order to discover damages at early stage of degradation. This method was compared with the method basing on measurement of vibrations.
The article is a continuation of the authors’ elaboration (Dąbrowski, Dziurdź, 2016). The aim of this continuation is to prove that a proposed way of modelling and using the coherent analysis to filter nonlinear disturbances is a useful technique in vibroacoustic diagnostics. The thesis was proved by solving the task of diagnosing the damage of the gear of the car gearbox on the basis of the measurement of mechanical vibrations and the noise in the engine chamber.
In industrial processes electrical motors are serviced after a specific number of hours, even if there is a need for service. This led to the development of early fault diagnostic methods. Paper presents early fault diagnostic method of synchronous motor. This method uses acoustic signals generated by synchronous motor. Plan of study of acoustic signal of synchronous motor was proposed. Two conditions of synchronous motor were analyzed. Studies were carried out for methods of data processing: Line Spectral Frequencies and K-Nearest Neighbor classifier with Minkowski distance. Condition monitoring is useful to protect electric motors and mining equipment. In the future, these studies can be used in other electrical devices.
The paper is a continuation of the publication under the title “Acoustic diagnostics applications in the study of technical condition of combustion engine” and concerns the detailed description of decision support system for identifying technical condition (type of failure) of specified combustion engine. The input data were measured sound pressure levels of specific faults in comparison to the noise generated by undamaged motor. In the article, the whole procedure of decision method based on game graphs is described, as well as the interface of the program for direct usage.
The paper presents the family of three analyzers allowing to measure impedance in the range of 10 Ω<|Zx|<10 GΩ in a wide frequency range from 10 mHz up to 100 kHz. The most important features of the analyzer family are: miniaturization, low power consumption, low production cost, telemetric controlling and the use of an impedance measurement method based on digital signal processing (DSP). The miniaturization and other above-mentioned features of the analyzers were obtained thanks to the use of the newest generation of large-scale integration chips: e.g. “system on a chip” microsystems (AD5933), 32-bit AVR32-family microcontrollers and specialized modules for wireless communication using the ZigBee standard. When comparing metrological parameters, the developed instrumentation can equal portable analyzers offered by top worldwide manufacturers (Gamry, Ivium) but outperforms them on smaller dimensions, weight, a few times lower price and the possibility to work in a distributed telemetric network. All analyzer versions are able to be put into medium-volume production.
This paper presents the bases of a new method of monitoring technical condition of turbomachine blades during their operation. The method utilizes diagnostic models such as a quotient of amplitude amplification and a phase shift of diagnostic signal y(t) which is a result of blade operation as well as a signal x(t) of blade environment while a blade tip approaches a sensor, amplitude amplification and phase shift of these signals while the blade tip moves away from the sensor. The adopted diagnostic models indirectly take into account the existing environment of a blade, represented by the signal x(t), without the need to measure it. Thus, the model is sensitive to the changes in technical condition of blades and practically intensive to a change in environment. The suggested method may prove very important in diagnostics of rotor blades during turbomachines operation (compressors, turbines etc.).
A fault diagnostics system of three-phase induction motors was implemented. The implemented system was based on acoustic signals of three-phase induction motors. A feature extraction step was performed using SMOFS-20-EXPANDED (shortened method of frequencies selection-20-Expanded). A classification step was performed using 3 classifiers: LDA (Linear Discriminant Analysis), NBC (Naive Bayes Classifier), CT (Classification Tree). An analysis was carried out for incipient states of three-phase induction motors measured under laboratory conditions. The author measured and analysed the following states of motors: healthy motor, motor with one faulty rotor bar, motor with two faulty rotor bars, motor with faulty ring of squirrel-cage. Measured and analysed states were caused by natural degradation of parts of the machine. The efficiency of recognition of the analysed states was good. The proposed method of fault diagnostics can find application in protection of three-phase induction motors.