Università degli studi di Modena e Reggio Emilia

This letter presents a 3-D-printed piezoelectric microphone with enhanced voltage sensitivity. The sensitivity is improved by a combination of a single-pillar mechanical design and a specific polyvinylidene fluoride (PVDF)-film electrode patterning. The moving part of the mechanical structure and the chassis are 3D-printed as a single unit and trimmed by laser cutting, allowing for a simple fabrication of the device. The measured sensitivity of 1 mV/Pa (±6 dB) in the bandwidth 500–2500 Hz agrees with simulations, showing an improvement over similar pillar-based piezoelectric sensor solutions. The sensitivity performance is shown to be comparable to existing microphones with different technologies. The microphone is also characterized by excellent linearity within the measurable range. 3D-printing technique can thus be adopted for the manufacturing of low cost and highly customizable microphone sensors.

This paper deals with the condition monitoring of knives via the Empirical Mode Decomposition (EMD). The cutting process is basically transient, thus Fourier Analysis and similar signal processing tools aren’t optimal because they treat signals as they were periodic. EMD is a signal analysis technique which is particularly suited for non-stationary and/or non-linear data, since it adaptively decomposes the signal in a sum of Intrinsic Mode Functions (IMFs). The knives under analysis are used inside an automated packaging machine; they are hydraulically actuated and are mounted on a moving support, so it’s not possible to put sensors on them because of security reasons related to sensors wiring. Instead, the actuators control valve is hosted on a fixed machine part, so its pressure signal is the one analysed in this paper. The sum of two IMFs is used to estimate the knife state and to obtain a representation of the wearing process during a knife life.

In this paper an elasto-dynamic model of a defective sphere bearing is presented. This two-dimensional model can simulate local faults on the bearing races and rolling elements, and it is based on the non-linear Hertzian contact deformation of the rolling elements. In the model the outer race is supposed to be fixed, and the rolling elements are supposed to roll without slipping. These assumptions yield a total of z + 4 Degrees of Freedom (DOF), where z is the number of rolling ele-ments: three DOF come from the inner race (two displacements and one rotation), one DOF from the cage (rotation) and one DOF from each rolling element (i.e., z radial displacements). Each contact between the spheres and the races is modelled by a non-linear spring (Hertz contact theory) and a damper proportional to the spring stiffness (Palmgren). The model uses a kinematic approach to calculate the trajectory of the rolling elements when passing over the defect. This trajectory is introduced into the equations of motion for the calculation of the rolling elements deformations; subsequently, the internal bearing forces are calculated. The model inputs are the bearing and defect geometry, the materials characteristics and the radial load. The model outputs the overall force transmitted to the outer race, which accurately reproduces the typical behaviour exhibited by a faulty bearing both in time and frequency domain

Rolling Elements Bearing (REB) condition monitoring is mainly based on the analysis of acceleration (vibration) signal in the load direction. This is one of the three components of the acceleration vector in 3D space: the main idea of this paper is the recovery of additional fault information from all the three acceleration vector components by combining them to obtain the modulus of the spatial acceleration (SAM) vector. The REB diagnostic performances of the SAM are investigated and compared to the load direction vibration by means of two rough estimators of the ‘‘Signal-to-Noise’’ ratio (SNR) and the Spectral Kurtosis. The SAM provides a higher SNR than the single load direction. Finally, Spectral Kurtosis driven Envelope analysis is performed for further comparison of the two signals: its results highlight that demodulation of the SAM isn’t strictly necessary to extract the fault features.

Empirical Mode Decomposition technique (EMD) is a recent development in non-stationary and non-linear data analysis. It is an algorithm which adaptively decomposes the signal in the sum of Intrinsic Mode Functions (IMFs) from which the instantaneous frequency can be easily computed. EMD has proven its effectiveness but is still affected from various problems. One of these is the “end-effect”, a phenomenon occurring at the start and at the end of the data due to the splines fitting on which the EMD is based. Various techniques have been tried to overcome the end-effect, like different data extension or mirroring procedures at the data boundary. In this paper we made use of the IMFs orthogonality property to apply a symmetrical window to the data before EMD for end-effect reduction. Subsequently the IMFs are post-processed to compensate for data alteration due to windowing. The simulations show that IMFs obtained with this method are of better quality near the data boundaries while remaining almost identical to classical EMD ones

The presence of air bubbles inside an internal gears pump usually leads to fast pump damage and breaking. When bubbles enter the high pressure chamber they may implode releasing pressure waves in the fluid that ultimately detach material from the gears and other crucial pump parts. This paper recalls and describes the activities performed in order to develop a condition monitoring system able to indicate the presence of air bubbles in an internal gear pump that feeds the hydraulic system of a packaging machine: this pump experienced a breakdown after only 3 minutes of air bubbles flowing in it. Since the machine is supposed to work continuously, an unexpected stop has heavy consequences in terms of loss of production time: therefore the machine producer decided to implement the condition monitoring (CM) of the pump. The producer’s goal is to use the warnings/alarms generated by the CM algorithm as a supplemental machine control signal: this may eventually command the stop of the machine to preserve it (and consequently the machine functionality) from fast damaging. The main phases of the development had been: data recording and analysis, diagnostic parameters identification, algorithm development and final algorithm validation in field tests. The data recording campaign included the simultaneous acquisition of 14 quantities of 5 different kinds (vibration, pressure, temperature, electrical torque and angular speed). The data analysis highlighted one of the vibration signals as the most significant to be monitored. Several signal features were analysed from the in order to evaluate their diagnostic capability point of view (i.e.: the ability to detect the presence of air bubbles in the pump); five features are used by the CM algorithm for the bubbles warning/alarm generation, these being the RMS, the bandpass filtered signal RMS, the Signal Entropy, the Spectral Mean Square Error and the Spectral Cumulative Difference. Currently, the algorithm is being tested and validated on recorded data and a field test campaign is being scheduled

Rolling Elements Bearing (REB) condition monitoring is mainly based on the analysis of acceleration (vibration) signal in the load direction. This is one of the three components of the acceleration vector in 3D space: the main idea of this paper is the recovery of additional fault information from the other two acceleration vector components by combining them to obtain the modulus of the spatial acceleration (SAM) vector. The REB diagnostic performances of the SAM are investigated and compared to the load direction of vibration by means a rough estimator of the “Signal-to-Noise” Ratio and the Spectral Kurtosis. The SAM provides a higher SNR than the single load direction. Finally, Spectral Kurtosis driven Envelope analysis is performed for further comparison of the two signals: its results highlight that demodulation of the SAM isn’t strictly necessary to extract the fault features, which are already available in the raw signal spectrum

Empirical Mode Decomposition technique (EMD) is a recent development in non-stationary and non-linear data analysis. It is an algorithm that adaptively decomposes the signal in the sum of Intrinsic Mode Functions (IMFs) from which the instantaneous frequency can be easily computed. Differently from the Fourier Transform, the EMD does not decompose a signal into stationary harmonic components, but into a finite and small number of IMFs based on the local characteristic time scale of the data. EMD has been widely applied in different fields as seismic studies, structural health monitoring, whether forecast, image processing and financial applications. More recently different authors proposed the use of EMD for condition monitoring purpose, e.g. in bearing or gear diagnostics. EMD has proven its effectiveness, but is still affected from various computational problems. One of these is the “end-effect”, a phenomenon occurring at the beginning and at the end of the data due to the splines fitting on which the EMD is based. Different spline fitting introduces different instantaneous frequency components that may unnecessarily complicate the data analysis. Various techniques have been tried to overcome the end-effect, like different data extension or mirroring procedures at the data boundary. This paper proposes a windowing process to manage the end-effect phenomenon. Recently other researchers have covered the windowing approach, suggesting classic windows like Hanning or flat-top, but results are highly dependent of the processed data. In this paper it has made use of the IMFs orthogonality property to apply a symmetrical polynomial window to the data before EMD for end-effect reduction. Subsequently the IMFs are post-processed to compensate for data alteration due to windowing. The procedure is applied to simulated data to easily take into account different kind of sources like trend, chirp, etc. The simulations show that IMFs obtained with this method may prove a reduced end-effect, while they are almost identical to classical EMD ones in other cases, depending on the data complexity.