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The position of an industrial robot in manufacturing has become more and more important. The monitoring of its condition is an urgent problem to be solved, because it is related to the safety and stability of production. Traditional monitoring methods mainly use external tools (such as acoustic emission sensor) or sensors (encoder, motor current sensor) inside industrial robot. In this paper, a condition monitoring method for industrial robots based on vibration analysis is proposed. Firstly, in the case of no fault, the three-axis acceleration data of the working point of the industrial robot is obtained by using a triaxial MEMS accelerometer. Based on experimental studies, it is shown that the vibration characteristics of each joint of the industrial robot have different characteristics, the differences being presented for different joints according to the characteristics of the frequency response. Then, different degrees of seal ring fault are simulated on the elbow of the robot. And the frequency and time domain responses are obtained based on different data. Through analysis, the fault location and degree of faults can be obtained according to vibration analysis. In addition, the accuracy of this conclusion is verified by calculating the RMS value of each axis. The results show that it is feasible to monitor the condition of industrial robots by vibration analysis.

Planet gear bearings are of critical components in the main gearbox of mission critical helicopters. Because of multiple excitation sources, weak cyclisation signatures and vibration transmission modulation (VTM) make it very challenging to diagnose the healthy conditions of planet bearings. This paper proposes a new diagnostic method based on modulation signal bispectrum (MSB) analysis. MSB can effectively suppress random noise by phase alignment and extract deterministic modulation effects at high frequency resolution, which allows the closely distributed and weak bearing signatures to be pull out with high accuracy. It thus can combine VTM effect with bearing signatures to enhance the weak signatures for achieving more reliable detection and diagnosis. Based on datasets from a multiple stage 1300 kW epicyclic gearbox employed in a commercial helicopter, this MSB based method is evaluated under different fault severity and operating conditions. It shows that the amplitudes coupled between fault frequency and epicycle carrier frequency can have more deterministic information regarding to bearing vibrations. Consequently, MSB coherences and magnitudes can jointly give accurate diagnostic results that are consistent with different fault severity and operating conditions, showing outstanding performance of MSB analysis.

Monitoring techniques focused on Internal Combustion (IC) engines such as diesel engines have since began gaining considerable interest. Amongst these techniques, vibration and acoustic emission (AE) methods have been widely explored particularly in application to the non-stationary signals of IC engines for fault detection. The individual contributions of both techniques have been illustrated separately in many researches. This paper presents an experimental comparison between these monitoring methodologies by using angular resampling and angular frequency representation to illustrated their fault detection capabilities on a diesel engine cold test. Analysis of both techniques could give an indication of presence of diesel engine journal bearing fault. Results reveals that implementation AE technique possess more potential in offering fault indication provided suitable and optimized signal processing techniques are utilized.

As one of the core components of CNC machine tools, the performance and status of the spindle have an important impact on the normal operation of the CNC machine tools. However, the spindle is often difficult to monitor its condition due to its complex structure and complex working conditions and failure modes. Therefore, the rotor sensing technology is used to monitor the state of the spindle, and the low-cost, low-power micro-electromechanical system is used to replace the traditional sensor (MEMS), so that the MEMS accelerometer can be directly installed on the rotating shaft, providing more accurate dynamics for the rotating machinery information. This article first discusses the rotor sensing technology, and then takes a certain type of electric spindle as the research object, analyzes its structure, establishes geometric models and dynamic equations, and uses ANSYS software to conduct modal analysis to determine the fault transmission path, And then studied the bearing faults with high failure rate and greater hazard in the electric spindle, and proposed a rolling bearing fault feature extraction method based on complementary set empirical mode decomposition (CEEMD) and modulation signal bispectrum. First, CEEMD is used to decompose several IMF components, and the correlation between each IMF and the original signal is calculated according to the correlation coefficient criterion, and the IMF components that meet the requirements are reconstructed. Finally, the reconstructed signal is decomposed by MSB and the fault characteristic frequency is extracted. Experiments have verified the analysis results of the CEEMD-Fast Spectral Kurtosis (FK) method. The CEEMD-MSB method proposed in this paper can obtain fault characteristics more accurately, which proves the effectiveness and superiority of the proposed method.

The quality and efficiency of high-speed machining are restricted by the matching performance of the motorized spindle-tool holder. In high speed cutting process, the mating surface is subjected to alternating torque, repeated clamping wear and centrifugal force, which results in serious degradation of mating performance. Therefore, for the purpose of the optimum maintenance time, periodic evaluation and prediction of remaining useful life (RUL) should be carried out. Firstly, the mapping model between the current of the motorized spindle and matching performance was extracted, and the degradation characteristics of spindle-tool holder were emphatically analyzed. After the original current is de-noised by an adaptive threshold function, the extent of degradation was identified by the amplitudes of wavelet packet entropy. A hybrid prognostics combining Relevance Vector Machine (RVM) i.e. AI-model with power regression i.e. statistical model was proposed to predict the RUL. Finally, the proposed scheme was verified based on a motorized spindle reliability test platform. The experimental results show that the current signal processing method based on wavelet packet and entropy can reflect the change of the degradation characteristics sensitively. Compared with other two similar models, the hybrid model proposed can accurately predict the RUL. This model is suitable for complex and high reliability equipment when Condition Monitoring (CM) data is scarcer.

Fault diagnosis based on a multiple-model (MM) approach is an analytical redundancy method. In this paper, we mainly focus on the model switch optimizing problem of the MM method. First, a MM method for gas turbine gas path fault diagnosis was proposed, and the gas turbine state space models are established based on analytical linearization which can simulate the nonlinear dynamic characteristics at the operating points. Then model soft switch method was proposed based on recursive Bayesian to solve the problem that the established state space model is accurate merely in the vicinity of the operating points, and generates the combined generic model for the full operating condition and make it smoother. Finally, the fault simulation was carried out on a marine gas turbine which shows that the proposed method can diagnose both single gas path fault and multiple gas path faults.

Internal combustion (IC) engine based powertrains are one of the most commonly used transmission systems in various industries such as train, ship and power generation industries. The powertrains, acting as the cores of machinery, dominate the performance of the systems; however, the powertrain systems are inevitably degraded in service. Consequently, it is essential to monitor the health of the powertrains, which can secure the high efficiency and pronounced reliability of the machines. Conventional vibration based monitoring approaches often require a considerable number of transducers due to large layout of the systems, which results in a cost-intensive, difficultly-deployed and not-robust monitoring scheme. This study aims to develop an efficient and cost-effective approach for monitoring large engine powertrains. Our model based investigation showed that a single measurement at the position of coupling is optimal for monitoring deployment. By using the instantaneous angular speed (IAS) obtained at the coupling, a novel fault indicator and polar representation showed the effective and efficient fault diagnosis for the misfire faults in different cylinders under wide working conditions of engines; we also verified that by experimental studies. Based on the simulation and experimental investigation, it can be seen that single IAS channel is effective and efficient at monitoring the misfire faults in large powertrain systems.

Due to long duty hours and stress cycling, crack fault often occurs in rotating shafts. On the other hand, uncertain factors are common in engineering mechanical systems. These uncertainties may have critical influences on the dynamics of the system and the crack fault will be more difficult to accurately detect. In this article, a finite element cracked rotating shaft model is studied considering uncertain factors and the Polynomial Chaos Expansion method is used to quantify their effects on the random vibration of the model. The stiffness of the elastic shaft and the imbalance force are taken as random parameters. Numerical results with different cases are presented. It can provide useful guidance for robust crack fault diagnosis.

Data denoising is essential to extract meaningful information and enhance diagnostic features in condition monitoring (CM). Hence, many techniques have been proposed for vibration data denoising. However, manual techniques require well-skilled labourers who will increase the cost of the monitoring process and may not always be available at the site. Denoising in the transformation domain using wavelets by thresholding has attracted researchers in recent years and been shown to be more effective than conventional filtering methods. However, estimation of the threshold value that can effectively suppress the noise has always been a hot research topic. One of the most promising approaches is to automate the process using adaptive and optimization methodologies. A new denoising method is proposed based on adaptive wavelet coefficients thresholding: a thresholding function is constructed, it adopts an adaptive procedure to estimate the initial threshold value based on the energy distribution of the signal; then the optimization procedure is adopted and the optimal threshold is selected through an iterative procedure by maximizing the harmonic-to-signal ratio (HSR). The new method is applied to both simulated data and real datasets collected from a defective tapered roller bearing and it is found that the proposed method increases the signal-to-noise ratio (SNR) significantly without distorting the signal when compared to benchmark thresholding methods.

To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.

Journal bearings as the important components are widely used in the rotating machinery. Early fault detection and diagnosis of journal bearings have received great more attention to ensure the high efficiency and safety operations. To overcome the shortages of low sensitivity from physical and chemical properties based monitoring, vibro-acoustics monitoring is focused on this study in that it can give real-time indication of tribodynamics of the bearing. In this paper, experimental investigations were carried out to monitor the lubrication regimes of journal bearings under varying loads ranged from 0 bar to 20 bar. Depending on the vibration analysis in the time domain (RMS) and short-time Fourier transform (STFT), the main effect of loads on the lubrication regimes can be found in the low frequency response (100 Hz–1000 Hz). Based on Stribeck curves, experimental results also allows for identifying the critical load under which the lubrication regime changes from full-film to mixed lubrication and even boundary lubrication, therefore, providing critical diagnostic features for online monitoring the lubricity of journal bearings.

The rolling element bearings are extensively applied in rotating machines, and they are the most susceptible components in rotating machines. Early fault detection of bearings is to prevent machines from such typical failures and subsequent consequences. In this paper a detector based on Ensemble Average of Autocorrelated Envelopes (EAAE) is proposed to identify the early occurrence faults in rolling element bearings, of which the fault induced vibration signals are inevitably contaminated or masked by both additive background noise and random phase noise (or slippage between bearing components). To enhance the cyclostationary characteristics for fault detection, it utilizes the phase synchronization property of autocorrelation signals for aligning the cyclostationary signals in the lag domain to achieve an effective ensemble average which allows both types of random influences to be suppressed significantly. As a result, this detector shows very high performance of robustness in extracting the local fault signatures, which is verified by simulation signals and experimental investigations and benchmarked by the recent milestone method of Spectral Correlation (SC).

This paper focuses on the vibration behaviors of a hollow-shaft rotor system in presence of an open crack under inherent model uncertainties. Non-probabilistic interval variables are used to represent the uncertain parameters, which releases the high demands of probabilistic knowledge in the traditional methods. In modeling the shaft, local stiffness matrix of the cracked element is derived by using the neutral axis method. The periodic response of the rotor system is solved by combination of the finite element method (FEM) and the harmonic balance method (HBM). A simple mathematical function, termed as the uncertain response surrogate function (URSF), is constructed to estimate the vibrational response in various cases where different parametric uncertainties are taken into consideration. In order to verify the robustness and accuracy of the URSF, the bounds of estimated response are compared with those obtained from the classical methods. Results show that the surrogate function has good accuracy and robustness, providing an effective method and guidance for diagnosing crack in uncertain context.

The problem of timely detecting the engine faults that make engine operating parameters exceed their control limits has been well-solved. However, in practice, a fault of a diesel engine can be present with weak signatures, with the parameters fluctuating within their control limits when the fault occurs. The weak signatures of engine faults bring considerable difficulties to the effective condition monitoring of diesel engines. In this paper, a multivariate statistics-based fault detection approach is proposed to monitor engine faults with weak signatures by taking the correlation of various parameters into consideration. This approach firstly uses principal component analysis (PCA) to project the engine observations into a principal component subspace (PCS) and a residual subspace (RS). Two statistics, i.e., Hotelling&rsquo, s T 2 and Q statistics, are then introduced to detect deviations in the PCS and the RS, respectively. The Hotelling&rsquo, s T 2 and Q statistics are constructed by taking the correlation of various parameters into consideration, so that faults with weak signatures can be effectively detected via these two statistics. In order to reasonably determine the control limits of the statistics, adaptive kernel density estimation (KDE) is utilized to estimate the probability density functions (PDFs) of Hotelling&rsquo, s T 2 and Q statistics. The control limits are accordingly derived from the PDFs by giving a desired confidence level. The proposed approach is demonstrated by using a marine diesel engine. Experimental results show that the proposed approach can effectively detect engine faults with weak signatures.