Miguel Delgado

Detection of Demagnetization Faults in Surface-Mounted Permanent Magnet Synchronous Motors by Means of the Zero-Sequence Voltage Component

This paper develops and analyzes an online methodology to detect demagnetization faults in surface-mounted permanent magnet synchronous motors. The proposed methodology, which takes into account the effect of the inverter that feeds the machine, is based on monitoring the zero-sequence voltage component of the stator phase voltages. The theoretical basis of the proposed method has been established. Attributes of the method presented here include simplicity, very low computational burden, and high sensibility. Since the proposed method requires access to the neutral point of the stator windings, it is especially useful when dealing with fault tolerant systems. A simple expression of the zero-sequence voltage component is deduced, which is proposed as a fault indicator parameter. Both simulation and experimental results presented in this paper show the potential of the proposed method to provide helpful and reliable data to carry out an online diagnosis of demagnetization failures in the rotor permanent magnets.

Dedicated hierarchy of neural networks applied to bearings degradation assessment

Condition monitoring schemes, able to deal with different sources of fault are, nowadays, required by the industrial sector to improve their manufacturing control systems. Pattern recognition approaches, allow the identification of multiple systems scenarios by means the relations between numerical features. The numerical features are calculated from acquired physical magnitudes, in order to characterize its behavior. However, only a reduced set of numerical features are used in order to avoid computational performance limitations of the artificial intelligence techniques. In this sense, feature reduction techniques are applied. Classical approaches analyze the features significance from a global data discrimination point of view. This paper, however, proposes a novel and reliable methodology to exploit the information contained in the original features set, by means a dedicated hierarchy of neural networks.

Intelligent sensor based on acoustic emission analysis applied to gear fault diagnosis

The development of intelligent and autonomous monitoring systems applied to rotating machinery, represents the evolution towards the automatic industrial plants supervision. In this regard, an acoustic emission based intelligent sensor is presented in this work. The proposed sensor records regularly the acoustic emission signal generated by gearboxes. A time domain statistical analysis is applied in order to characterize the acquired data. Afterwards, a neural network based algorithm is applied to detect gear fault patterns. Finally, the diagnosis result is sent through a wireless transceiver to the central control unit. Moreover, in order to reach a real autonomous operation, the sensor power is approached by different energy harvesting solutions.

Evaluation of feature calculation methods for electromechanical system diagnosis

The use of intelligent machine health monitoring schemes is increasing in critical applications as traction tasks in the transport sector. The high diagnosis capability and reliability required in these systems are being supported by intelligent classification algorithms. These classifiers use calculated features from the system to perform the diagnosis. In this context, different features calculation methods can be applied to characterize the system condition obtaining different classification results. The aim of this work is based on diagnosis capabilities evaluation of the main features calculation methods: statistical features from time, statistical features from frequency, time-frequency distributions and signal decomposition techniques. The features capabilities are quantitatively evaluated by two parameters: the classification accuracy and the discriminant coefficient. Experimental results are obtained from an electromechanical actuator under different diagnosis requirements: from single fault to combined faults detection under stationary and non-stationary speed and torque conditions.

Industrial Time Series Modelling by Means of the Neo-Fuzzy Neuron

Industrial process monitoring and modeling represent a critical step in order to achieve the paradigm of zero defect manufacturing. The aim of this paper is to introduce the neo-fuzzy neuron method to be applied in industrial time series modeling. Its open structure and input independence provide fast learning and convergence capabilities, while assuring a proper accuracy and generalization in the modeled output. First, the auxiliary signals in the database are analyzed in order to find correlations with the target signal. Second, the neo-fuzzy neuron is configured and trained accordingly by means of the auxiliary signal, past instants, and dynamics information of the target signal. The proposed method is validated by means of real data from a Spanish copper rod industrial plant, in which a critical signal regarding copper refrigeration process is modeled. The obtained results indicate the suitability of the neo-fuzzy neuron method for industrial process modeling.

Accurate bearing faults classification based on statistical-time features, curvilinear component analysis and neural networks

Bearing faults are the commonest form of malfunction associated with electrical machines. So far, the research has been carried out mainly in the detection of localized faults, but the diagnosis of distributed faults is still under development. In this context, this work presents a new scheme for detecting and classifying both kinds of faults. This work deals with a new diagnosis monitoring scheme, which is based on statistical-time features calculated from vibration signal, curvilinear component analysis for compression and visualization of the features behavior and a hierarchical neural network structure for classification. The obtained results from different operation conditions validate the effectiveness and feasibility of the proposed methodology.

Remaining useful life estimation of ball bearings by means of monotonic score calibration

The estimation of remaining useful life applied to industrial machinery and its components is one of the current trends in the advanced manufacturing field. In this context, this work presents a reliable methodology applied to ball bearings health monitoring. First, the proposed methodology analyses the available vibration and temperature data by means of the Spearman coefficient. This step allows the identification of the most significant monotonic relationship between features and the evolution of the remaining useful life. The method is complemented by means of the application of one-class support vector machine in order to obtain the remaining useful life indication trough the mapping of the classification scores. The proposed scheme shows a significant accuracy and reliability of the degradation detection due to the coherent management of the information. This fact is experimentally demonstrated by a run-to-failure test bench and the comparison with classical approaches.

Distributed neuro-fuzzy feature forecasting approach for condition monitoring

The industrial machinery reliability represents a critical factor in order to assure the proper operation of the whole productive process. In regard with this, diagnosis schemes based on physical magnitudes acquisition, features calculation, features reduction and classification are being applied. However, in this paper, in order to enhance the condition monitoring capabilities, a forecasting approach is proposed, in which not only the current status of the system under monitoring in identified, diagnosis, but also the future condition is assessed, prognosis. The novelties of the proposed methodology are based on a distributed features forecasting approach by means of adaptive neuro-fuzzy inference system models. The proposed method is validated by means of an accelerated bearing degradation experimental platform.

Novelty detection methodology based on multi-modal one-class support vector machine

The lack of information of complicated industrial systems represents one of the main limitation to implement condition monitoring and diagnosis systems. Novelty detection framework plays an essential role for monitoring systems in which the information about the different operation conditions or fault scenarios is unavailable or limited. In this context, this work presents a novelty detection approach applied to a main rotatory element of an industrial packaging machine, a camshaft. The developed novelty detection method begins with the assumption that only data corresponding to a healthy operation of the machine is available, and the objective is to detect anomalies in the behavior of the machine. To monitor the packing machine, first, the current signals acquired from the main motor are processed by means of a normalized time-frequency map. Next, a set of features are calculated from the frequency maps. Then a set of novelty models are trained. When abnormal data is detected, an alarm will be activated to be confirmed by the user. The proposed methodology includes the re-training of the novelty detection models to include such behaviors. The proposed methodology shows a good performance to identify abnormal behavior on the machine and successfully incorporate novel scenarios.

Diagnosis method based on topology codification and neural network applied to an industrial camshaft

Since the last years, there is an increasing interest from the industrial sector to provide the electromechanical systems with diagnosis capabilities. In this context, this work presents a novel monitoring scheme applied to diagnose faults in the main rotatory element of an industrial packaging machine, the camshaft. The developed diagnosis method considers a coherent procedure to process the acquired measurement. First, the current signals acquired from the main motor are processed in a normalized time-frequency map. Next, the characteristics fault patterns are identified and numerically characterized. A double self-organized map structure is proposed to manage the information till compress it to just two features by means of a topology codification of the data space. Finally, a neural network based classification algorithm is used to classify the condition of the camshaft. The effectiveness of this condition monitoring scheme has been verified by experimental results obtained from industrial machinery.