Mounting of accelerometers with structural adhesives: experimental characterization of the dynamic response

The use of accelerometers to monitor the vibrations of either complex machinery or simple components involves some considerations about the mounting of the sensor to the structure. Different types of mounting solutions are commonly used, but in all cases they can be classified in one of these categories: stud mounting, screw mounting, adhesive mounting, magnetic mounting, and probe sensing. Indeed, each of them has a specific field of application depending on e.g. the mounting surface conditions, the temperature, the accessibility to the specific mounting point, etc. The choice of the mounting solution has an important effect on the accuracy of the usable frequency response of the accelerometer, since the higher the stiffness of the fixing, the higher the low-pass frequency limit of the mounting. This article specifically focuses on adhesive mounting of accelerometers, which includes a great number of different products from the temporary adhesives like the beeswax to the permanent ones like cyanoacrylate polymers. Among the variety of commercial adhesives, three specific products have been experimentally compared to assess their transmissivity and the results are reported in this article. A two-component methylmethacrylate (HBM X60), a modified silane (Terostat 737), and a cyanoacrylate (Loctite 454) adhesive have been used to join two aluminum bases, one connected to an accelerometer and the other to the head of electromagnetic shaker. A design of experiment (DOE) approach was used to test the system at several levels of amplitude and frequency of the external sinusoidal excitation supplied by the shaker

Effect of Temperature on the Dynamic Response of Adhesively Mounted Accelerometers

This paper focuses on the effect of temperature on the frequency response function (FRF) of three different structural adhesives; namely a two component methylmethacrylate (HBM X60), a modified silane (Terostat 939) and a cyanoacrylate (Loctite 454). The structural adhesives are commonly used in vibration analysis to mount accelerometers on structures or machines. The stiffness of the adhesive can influence the response function on large frequency band, affecting the proportional excitation between the structure and the accelerometer. In the “system structure + adhesive + accelerometer”, the adhesive may acts like a filter between the source and the sink of vibrations. A variation of the dynamic response of the filter could lead to an erroneous analysis. The authors already investigated the relation between the frequency response function and operating conditions of the test. This paper expands the research by considering the temperature effect in order to depict a complete picture of the adhesive behavior on dynamic response of an accelerometer. A design of experiments (DOE) approach was used to test two bonded aluminum bases at different levels of temperature and frequency of the external sinusoidal excitation, supplied by an electromagnetic shaker. The results clearly demonstrate that the adhesive is not able to change the system response, therefore the signal transmission is good in the entire range of temperature regardless the adhesive chosen

Anomaly detection in a cutting tool by K-means clustering and Support Vector Machines

This paper concerns the analysis of experimental data, verifying the applicability of signal analysis techniques for condition monitoring of a packaging machine. In particular, the activity focuses on the cutting process that divides a continuous flow of packaging paper into single packages. The cutting process is made by a steel knife driven by a hydraulic system. Actually, the knives are frequently substituted, causing frequent stops of the machine and consequent lost production costs. The aim of this paper is to develop a diagnostic procedure to assess the wearing condition of blades, reducing the stops for maintenance. The packaging machine was provided with pressure sensor that monitors the hydraulic system driving the blade. Processing the pressure data comprises three main steps: the selection of scalar quantities that could be indicative of the condition of the knife. A clustering analysis was used to set up a threshold between unfaulted and faulted knives. Finally, a Support Vector Machine (SVM) model was applied to classify the technical condition of knife during its lifetime

An algorithm for the simulation of faulted bearings in non-stationary conditions

In the field of condition monitoring the availability of a real test-bench is not so common. Furthermore, the early validation of a new diagnostic technique on a proper simulated signal is crucial and a fundamental step in order to provide a feedback to the researcher and to increase the chances of getting a positive result in the real case. In this context, the aim of this paper is to detail a step-by-step analytical model of faulted bearing that the reader could freely and immediately use to simulate different faults and different operating conditions. The vision of the project is a set of tools accepted by the community of researchers on condition monitoring, for the preliminary validation of new diagnostics techniques. The tool proposed in this paper is focused on ball bearing, and it is based on the well-known model published by Antoni in 2007. The features available are the following: selection of the location of the fault, stage of the fault, cyclostationarity of the signal, random contributions, deterministic contributions, effects of resonances in the machine and working conditions (stationary and non-stationary). The script is provided for the open-source Octave environment. The output signal is finally analysed to prove the expected features

Condition monitoring and reliability of a resistance spot welding process

The reliability of a resistance spot welding (RSW) process is studied monitoring the quality of the corresponding welding points. Each welding point is uniquely represented by a specific resistance characteristic curve over time. Five learning resistance characteristic curves, the good quality of the related welding points was experimentally verified by means of a non-destructive technique, are selected as a reference to check the quality of welding points related to different process resistance characteristic curves. A first estimate of the quality of the welding point is made comparing the corresponding process resistance characteristic curve with the learning maximum, minimum and average resistance characteristic curves. Both good quality and defective (glued or squeezed) welding points are observed. In order to more correctly identify the quality level of each welding point, two different parameters comparing the related process resistance characteristic curve with the learning average resistance characteristic curve are applied. First, the residual resistance, as the difference at each instant of time between the two resistance characteristic curves, is considered. Then, the Euclidean distance, as the geometric distance at each instant of time between the two resistance characteristic curves, is adopted. Finally, the trend of the quality of the welding points as their number increases for welding electrodes with a fixed number of dressings is investigated

Statistical evidence of central moment as fault indicators in ball bearing diagnostics

This paper deals with post processing of vibration data coming from a experimental tests. An AC motor running at constant speed is provided with a faulted ball bearing, tests are done changing the type of fault (outer race, inner race and balls) and the stage of the fault (three levels of severity: from early to late stage). A healthy bearing is also measured for the aim of comparison. The post processing simply consists in the computation of scalar quantities that are used in condition monitoring of mechanical systems: variance, skewness and kurtosis. These are the second, the third and the fourth central moment of a real-valued function respectively. The variance is the expectation of the squared deviation of a random variable from its mean, the skewness is the measure of the lopsidedness of the distribution, while the kurtosis is a measure of the heaviness of the tail of the distribution, compared to the normal distribution of the same variance. Most of the papers in the last decades use them with excellent results. This paper does not propose a new fault detection technique, but it focuses on the informative content of those three quantities in ball bearing diagnostics from a statistical point of view. In this paper, a discriminant function analysis is used, to determine which central moment has a high discrimination power in the diagnostics of ball bearing in stationary conditions

Experimental Evidence of the Speed Variation Effect on SVM Accuracy for Diagnostics of Ball Bearings

In recent years, we have witnessed a considerable increase in scientific papers concerning the condition monitoring of mechanical components by means of machine learning. These techniques are oriented towards the diagnostics of mechanical components. In the same years, the interest of the scientific community in machine diagnostics has moved to the condition monitoring of machinery in non-stationary conditions (i.e., machines working with variable speed profiles or variable loads). Non-stationarity implies more complex signal processing techniques, and a natural consequence is the use of machine learning techniques for data analysis in non-stationary applications. Several papers have studied the machine learning system, but they focus on specific machine learning systems and the selection of the best input array. No paper has considered the dynamics of the system, that is, the influence of how much the speed profile changes during the training and testing steps of a machine learning technique. The aim of this paper is to show the importance of considering the dynamic conditions, taking the condition monitoring of ball bearings in variable speed applications as an example. A commercial support vector machine tool is used, tuning it in constant speed applications and testing it in variable speed conditions. The results show critical issues of machine learning techniques in non-stationary conditions