Machine learning algorithms (MLAs) are increasingly being used as effective techniques for processing vibration signals obtained from complex industrial machineries. Previous applications of automatic fault detection algorithms in the diagnosis of rotating machines were mainly based on historical operating data sets, limiting the diagnostic reliability to devices with an extended operating history. Moreover, physically collected data are often restricted by the conditions of acquisition and the specific elements for which they were recorded. Digital twin (DT) provides a powerful tool able to generate a huge amount of training data for MLAs. However, the DT model must be accurate enough to substitute the experiments. This work aims to escape the experience requirement by using a simulation-driven MLA based on the multifactorial analysis of fault indicators associated with a DT. To achieve this approach, a numerical model of a rotor-ball bearing system is developed. The latter is updated according to a parameter update scheme based on a comparison between the relevant features of the experimentally measured signals and the signals simulated by the model. These features are chosen as the selected input parameters of the MLA classifier. The results show that after updating, the developed DT has provided a reliable diagnostic with an adaptive degradation analysis, which makes the simulated data suitable for the construction of a machine learning predictive model. Two common MLAs, (multi-kernel support vector machine) and (k nearest neighbor’s algorithm), were trained using the simulated data and validated later against experimental datasets.
Due to the wide use of gearmotor systems in industry, many diagnostic techniques have been developed/employed to prevent their failures. An insufficient lubrication of gearboxes of these machines could shorten their life and lead to catastrophic failures and losses, making it important to ensure a required lubrication level. For the first time in worldwide terms, this paper proposed to diagnose a lack of gearbox oil lubrication using motor current signature analysis (MCSA). This study proposed, investigated, and experimentally validated two new technologies to diagnose a lack of lubrication of gear motor systems based on MCSA. Two new diagnostic features were extracted from the current signals of a three-phase induction motor. The effectiveness of the proposed technologies was evaluated for different gear lubrication levels and was compared for three phases of motor current signals and for a case of averaging the proposed diagnostic features over three phases. The results confirmed a high effectiveness of the proposed technologies for diagnosing a lack of oil lubrication in gearmotor systems. Other contributions were as follows: (i) it was shown for the first time in worldwide terms, that the motor current nonlinearity level increases with the reduction of the sgearbox oil level; (ii) novel experimental validations of the proposed two diagnostic technologies via comprehensive experimental trials (iii) novel experimental comparisons of the diagnosis effectiveness of the proposed two diagnostic technologies.
Abstract-Recently, the approaches based on source separation are increasingly adopted for the fault diagnosis in several industrial applications. In particular, Independent Component Analysis (ICA) method is attractive, thanks to its simplicity of implementation. In the context of electrical rotating machinery with a variable speed, namely the wind turbine type, the interaction between the electrical and mechanical parts along with the fault is complex. Therefore, the essential system variables are affected and it thereby requires to be analyzed in order to detect the presence of certain faults. In this paper, the target system is the classical association of a doubly-fed induction motor to a two stage gearbox for wind energy application system. The investigated mechanical fault is a uniform wear of two gear wheels for the same stage. The idea behind the proposed technique is to consider the fault detection and identification as a source separation problem. Based on the analysis into independent components, Fast-ICA algorithm is adopted to separate and identify the sources of the gear faults. Afterwards, a spectral analysis is applied on the signals resulting from the separation in order to identify the fault components related to the damaged wheels. The efficiency of the proposed technique for the separation and identification of the fault components is evaluated by numerical simulations.
Condition monitoring of rotating machinery plays an important role in reducing catastrophic failures and production losses in the 4.0 Industry. Vibration analysis has proven to be effective in diagnosing rotating machine failures. However, identifying bearing defects based on vibration analysis remains a difficult task, especially in non-stationary operation conditions. This work aims to automate the process of identifying bearing defects under variable operating speeds. Based on an order analysis technique, three frequency domain features: Spectrum peak Ratio Outer (SPRO), Spectrum peak Ratio Inner (SPRI), and Spectrum peak Ratio Rolling element (SPRR) are updated to perform with non-stationary signals. The updated features are extracted from vibration data of a real ball bearing system. They are retained to build a predictive multi-kernel support vector machine (MSVM) classification model. Therefore, the effectiveness of the proposed features is evaluated based on the performance of the constructed classifier. The updated features deployed have proven their effectiveness in identifying bearing: outer race, inner race, ball, and combined defects under variable speed conditions.
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