Misael Lopez-Ramirez

Novel FPGA-based Methodology for Early Broken Rotor Bar Detection and Classification Through Homogeneity Estimation

Early detection of induction-motor faults has been an increasing matter of research in the last few years. The reliable identification of broken rotor bars (BRB) is still under investigation as it is one of the most common and difficult-to-detect faults in induction motors. Many methods have been proposed to deal with this issue. Recent approaches combine techniques looking for improving the performance of the diagnosis. Their major disadvantage is the high computational requirements, which restrains them from being used in online detection. The contribution in this paper is twofold. The first one is a novel methodology for induction motor BRB detection and the fault severity classification using homogeneity as index, which, to the best of our knowledge, has never been used as an indicator for fault diagnosis, analyzing one phase of the induction motor startup-transient current. Because of the low computational complexity in homogeneity calculation, the second contribution of this paper is a hardware-processing unit based on a field programmable gate array device for online detection and classification of BRB. Obtained results demonstrate the high efficiency of the proposed methodology as a deterministic technique for incipient BRB diagnosis in induction motors, which can detect and differentiate among half, one, or two BRBs with a certainty greater than 99.7%.

Gabor and the Wigner-Ville transforms for broken rotor bars detection in induction motors

Induction Motors are subjected to unavoidable stresses that create failures in their different parts and result in substantial cost penalties. An effective incipient fault detection technique can reduce the maintenance expenses by preventing high cost failures and unscheduled downtimes. Broken rotor bars (BRB) is the most common rotor-related failure, and the startup transient is suitable for their detection. Therefore, several time-frequency representations have been proposed for this aim. This paper presents a performance comparison between the Gabor transform and the Wigner-Ville with a Choi-Williams kernel (WVD-CW) transform for broken rotor bar detection in induction motors. Preliminary results show that WVD-CW has better time-frequency resolution than the Gabor transform ensuring the BRB fault detection with high certainty.

FPGA-based reconfigurable unit for real-time power quality index estimation

Power quality monitoring is an important subject for investigation and research. In this work a generic FPGA-based portable architecture for real-time power quality index (PQI) estimation is proposed. Different from off-the-shelf specialized equipment, the proposed hardware implementation offers higher exactitude for PQI estimation and representation of voltage and current signals. Unlike previous approaches, the proposed FPGA-based PQI computation unit estimates up to fourteen PQI, it is highly portable to different platforms, and it can be implemented on a single chip.

Detection and diagnosis of lubrication and faults in bearing on induction motors through STFT

The Induction motors are nowadays widely used in a variety of industrial applications due to their simple build and high reliability, On the other hand, adequate bearing lubrication is so important to guarantee an adequate operation during a long time. Thus, in this work, a method for detection and diagnosis of lubrication and mechanical faults in bearing used on induction motors through Short Time Fourier Transform (STFT) is proposed. Obtained results demonstrate the correct detection of lubrication and mechanical faults in bearing by the identification of different spectral components for each faulty case and the healthy condition.

FPGA-based reconfigurable unit for image encryption using orthogonal functions

Encryption is an important tool in many areas of application and research. The advances in communications have encouraged researchers to find new techniques for providing data security, confidentiality, integrity and authentication. The techniques proposed until now for image encryption apply well-known image processing techniques, increasing their computational complexity and processing time, threatening their use on real-time applications. On the other hand, the already proposed hardware implementations for image encryption do not allow portability to distinct FPGA platforms, and they do not guarantee high speed and optimal resource utilization. In this work, a generic real-time, FPGA-based, reconfigurable architecture for online image encryption using orthogonal functions is proposed. The introduced architecture implements a novel highly-efficient algorithm for high speed image encryption using minimal resources, which is portable to different FPGA platforms from distinct vendors. Obtained results demonstrate the effectiveness of the proposed approach on different cases of study, reaching processing rates up to 39 frames per second.

Extended depth of field in images through complex amplitude pre-processing and optimized digital post-processing

Many applications require images with high resolution and an extended depth of field. Directly changing the depth of field in optical systems results in losing resolution and information from the captured scene. Different methods have been proposed for carrying out the task of extending the depth of field. Traditional techniques consist of optical-system manipulation by reducing the pupil aperture along with the image resolution. Other methods propose the use of optical arrays with computing-intensive digital post-processing for extending the depth of field. This work proposes a pre-processing optical system and a cost-effective post-processing digital treatment based on an optimized Kalman filter to extend the depth of field in images. Results demonstrate that the proposed pre-processing and post-processing techniques provide images with high resolution and extended depth of field for different focalization errors without requiring optical system calibration. In assessing the resulting image through the universal image quality index, this technique proves superior.

FPGA-based reconfigurable unit for image filtering in frequency domain

Digital filtering is a key step of image processing in many applications. Due to its importance in this work a general FPGA-based reconfigurable architecture for real-time, online image filtering in the frequency domain is presented. The proposed FPGA-based implementation is portable to distinct platforms from different vendors. Obtained results from different study cases show the high capability and performance of the proposed hardware implementation by applying any user designed filtering operation on an image, and outperforming by two orders of magnitude its software implementation counterpart.

Discriminating the lubrication condition from the rotor bearing fault in induction motors using Margenau-Hill frequency distribution and artificial neural networks

Purpose About 13 to 44 per cent of motor faults are caused by bearing failures in induction motors (IMs), where lubrication plays a significant role in maintaining rotating equipment because it minimizes friction and prevents wear by separating parts that move next to each other, and more than 35 per cent of bearing failures can be attributed to improper lubrication. An excessive amount of grease causes the rollers or balls to slide along the race instead of turning, and the grease will actually churn. This churning action will eventually wear down the base oil of the grease and all that will be left to lubricate the bearing is a thickener system with little or no lubricating properties. The heat generated from the churning, insufficient lubricating oil will begin to harden the grease, and this will prevent any new grease added to the bearing from reaching the rolling elements, with the consequence of bearing failure and equipment downtime. Regarding the case of grease excess in bearings, this case has not been sufficiently studied. This work aims to present an effective methodology applied to the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the Margenau-Hill distribution (MHD) and artificial neural networks (ANNs), where the obtained results demonstrate the correct classification of the studied cases. Design/methodology/approach This work proposed an effective methodology applied to the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the MHD and ANNs. Findings In this paper, three cases of study for a bearing in an IM are studied, detected and classified correctly by combining some methods. The marginal frequency is obtained from the MHD, which in turn is achieved from the stator current signal, and a total of six features are estimated from the power spectrum, and these features are forwarded to the designed ANN with three output neurons, where each one represents a condition in the IM: healthy bearing, mechanical bearing fault and excessively lubricated bearing. Practical implications The proposed methodology can be applied to other applications; it could be useful to use a time–frequency representation through the MHD for obtaining the energy density distribution of the signal frequency components through time for analysis, evaluation and identification of faults or conditions in the IM for example; therefore, the proposed methodology has a generalized nature that allows its application for detecting other conditions or even multiple conditions under different working conditions by a proper calibration. Originality/value The lubrication plays a significant role in maintaining rotating equipment because it minimizes friction and prevents wear by separating parts that move next to each other, and more than 35 per cent of bearing failures can be attributed to improper lubrication and it negatively affects the efficiency of the motor, resulting in higher operating costs. Therefore, in this work, a new methodology is proposed for the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the MHD and ANNs. The proposed methodology uses a total of six features estimated from the power spectrum, and these features are sent to the designed ANN with three output neurons, where each one represents a condition in the IM: healthy bearing, mechanical bearing fault and excessively lubricated bearing. From the obtained results, it was demonstrated that the proposed approach achieves higher classification performance, compared to short-time Fourier transform, Gabor transform and Wigner-Ville distribution methods, allowing to identify mechanical bearing faults and bearing excessively lubricated conditions in an IM, with a remarkable 100 per cent effectiveness during classification for treated cases. Also, the proposed methodology has a generalized nature that allows its application for detecting other conditions or even multiple conditions under different working conditions by a proper calibration.

FPGA-based methodology for depth-of-field extension in a single image

Abstract Computer vision applications rely upon high resolution images with extended depth of field (DoF). Most approaches contain arrays of lenses and computing intensive algorithms that must be calibrated every time, to reach in-focus images; however, by changing directly the system focal length, resolution and information are lost. Traditional methods consist in taking a great number of images varying the optical system pupil aperture, whereas, the post processing system demands a great amount of computational resources with long processing time and high implementation cost. In this work a novel methodology for DoF extension that applies a complex-amplitude mask during a single image pre-processing taken at full pupil aperture, and a Wiener filter for the image recovery without focalization errors, during post-processing, is introduced. An FPGA-based implementation shows the feasibility of the proposed methodology for real-time DoF extension. Obtained results demonstrate qualitatively and quantitatively the effectiveness of the proposed FPGA-based method, which offers a reconfigurable solution for online DoF extension on a single image, in real time.

FPGA-based hardware processing unit for time-frequency representation of a signal through Wigner-Ville distribution

In some applications, it is necessary to use a time-frequency representation (TFR) for showing the energy density distribution of the frequency components through time. The Wigner-Ville distribution (WVD) is a quadratic TFR, which has been recently of great interest for researchers, due to its independence from the kind or size of the window chosen, and its inherent suitability for analyzing non-stationary signals. However, the WVD computational complexity makes it extremely time-consuming processes. In this work, a generic FPGA-based portable architecture for calculating the WVD and the corresponding ambiguity function is proposed. To assess the effectiveness and performance of the proposed generic hardware architecture different study cases are considered. Obtained results demonstrate the versatility and usefulness of the proposed FPGA-based, reconfigurable architecture for real-time computation of WVD.