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Before marketing external gear pumps are subjected to a running in process to increase their efficiency. However, this is one of the most time-consuming tasks of the entire manufacturing process. Therefore, a mathematical model for optimizing the running in process can be a useful tool for time-to-market reduction. In particular, in this paper a model for the analysis of the dynamic behaviour of external gear pumps, developed by the authors in previous works, is modified and used for simulating the running in process. The modified model is presented and validated via experimental data. A good correlation between simulation and test results guarantees the effectiveness of the model in determining the amount and the distribution of the removed material during the running in process. A meaningful reduction (16%) of the global running in time has been achieved with the introduction of a modified running in process drawn from simulation results

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This paper addresses the use of several signal processing tools for monitoring and diagnosis of assembly faults in diesel engines through the cold test technology. One specific fault is considered here as an example: connecting rod with incorrectly tightened screws. First, the experimental apparatus concerning the vibration tests is introduced. Subsequently, the dynamic analysis of the engine has been carried out in order to calculate the connecting rod forces against the crankpin for predicting the position where mechanical impacts are expected. Then, a vibration signal model for this type of faults is introduced. It deals with the cyclostationary model in which the signal is subdivided into two main parts: deterministic and nondeterministic. Finally, the acceleration signals acquired from the engine block during a cold test cycle at the end of the assembly line are analyzed. For quality control purposes in order to obtain reliable thresholds for the pass/fail decision, a method based on the image correlation of symmetrized dot patterns is proposed. This method visualizes vibration signals in a diagrammatic representation in order to quickly detect the faulty engines in cold tests. Moreover, the fault identification is discussed on the basis of the cyclostationary model of the signals. The first-order cyclostationarity is exploited by the analysis of the time synchronous average (TSA). In addition, the residual signal is evaluated by subtracting the TSA from the raw synchronized signal, and thus, the second-order cyclostationarity analysis is developed by means of the Wigner–Ville distribution (WVD), Wigner–Ville spectrum (WVS), and mean instantaneous power. Moreover, continuous wavelet transform is presented and compared with the WVD and WVS.

This work seeks to study the potential effectiveness of the Blind Signal Extraction (BSE) as a pre-processing tool for the\ud detection of distributed faults in rolling bearings. In the literature, most of the authors focus their attention on the\ud detection of incipient localized defects. In that case, classical techniques (i.e. envelope analysis) are robust in recognizing\ud the presence of the fault and its characteristic frequency. However, when the fault grows, the classical approach fails, due\ud to the change of the fault signature. De facto, in this case the signal does not contain impulses at the fault characteristic\ud frequency, but more complex components with strong non-stationary contents. Moreover, signals acquired from complex\ud machines often contain contributions from several different components as well as noise; thus the fault signature can\ud be hidden in the complex system vibration. Therefore, pre-processing tools are needed in order to extract the bearing\ud signature, from the raw system vibration. In this paper the authors focalize their attention on the application of the BSE in\ud order to extract the bearing signature from the raw vibration of mechanical systems. The effectiveness and sensitivity of\ud BSE is here exploited on the basis of both simulated and real signals. Among different procedures for the BSE computation,\ud the Reduced-Rank Cyclic Regression algorithm (RRCR) is used. Firstly a simulated signal including the effect of\ud gear meshing as well as a localized fault in bearings is introduced in order to tune the parameters of the RRCR. Next, two\ud different real cases are considered, a bearing test-rig as an example of simple machine and a gearbox test-rig as an\ud example of complex machine. In both examples, the bearings were degreased in order to accelerate the wear process.\ud The BSE is compared with the usual pre-processing technique for the analysis of cyclostationary signals, i.e. the extraction\ud of the residual signal. The fault detection is carried out by the computation of the Integrated Cyclic Modulation\ud Spectrum on the extracted signals. The results indicate that the extracted signals via BSE clearly highlight the distributed\ud fault signature, in particular both the appearance of the faults as well as their development are detected, whilst noise still\ud hides fault grow in the residual signals

This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra of vibration signals to detect and diagnose bearing faults. In this paper, a novel vibration-based BFDD via a probability plot (ProbPlot) image recognition technique under constant and variable speed conditions is proposed. The proposed technique is based on the absolute value principal component analysis (AVPCA), namely, ProbPlot via image recognition using the AVPCA (ProbPlot via IR-AVPCA) technique. A comparison of the features (images) obtained: (1) directly in the time domain from the original raw data of the vibration signals; (2) by capturing the Fast Fourier Transformation (FFT) of the vibration signals; or (3) by generating the probability plot (ProbPlot) of the vibration signals as proposed in this paper, is considered. A set of realistic bearing faults (i.e., outer-race fault, inner-race fault, and balls fault) are experimentally considered to evaluate the performance and effectiveness of the proposed ProbPlot via the IR-AVPCA method.