Jesus Roberto Millan-Almaraz

FPGA-Based Multiple-Channel Vibration Analyzer for Industrial Applications in Induction Motor Failure Detection

Early detection of failures in equipment is one of the most important concerns to industry. Many techniques have been developed for early failure detection in induction motors. There is the necessity of low-cost instrumentation for online multichannel measurement and analysis of vibration in the frequency domain, and this could be fixed to the machine for continuous monitoring to provide a reliable continuous diagnosis without needing trained staff. Field-programmable gate arrays (FPGAs) are distinguished by being very fast and highly reconfigurable devices, allowing the development of scalable parallel architectures for multichannel analysis without changing the internal hardware. The novelty of this work is the development of a low-cost FPGA based on a multichannel vibration analyzer; this is capable of providing an automatic diagnosis of the motor state carrying out online continuous monitoring. To test the functionality of the proposed vibration analyzer, three experiments on 746-W (1-hp) induction motors were carried out. Such experiments are intended to detect motor failures such as broken bars, unbalance, and looseness. The obtained results show the overall system performance.

Instrumentation in developing chlorophyll fluorescence biosensing: a review.

Chlorophyll fluorescence can be defined as the red and far-red light emitted by photosynthetic tissue when it is excited by a light source. This is an important phenomenon which permits investigators to obtain important information about the state of health of a photosynthetic sample. This article reviews the current state of the art knowledge regarding the design of new chlorophyll fluorescence sensing systems, providing appropriate information about processes, instrumentation and electronic devices. These types of systems and applications can be created to determine both comfort conditions and current problems within a given subject. The procedure to measure chlorophyll fluorescence is commonly split into two main parts; the first involves chlorophyll excitation, for which there are passive or active methods. The second part of the procedure is to closely measure the chlorophyll fluorescence response with specialized instrumentation systems. Such systems utilize several methods, each with different characteristics regarding to cost, resolution, ease of processing or portability. These methods for the most part include cameras, photodiodes and satellite images.

Smart Sensor for Real-Time Quantification of Common Symptoms Present in Unhealthy Plants

Plant responses to physiological function disorders are called symptoms and they are caused principally by pathogens and nutritional deficiencies. Plant symptoms are commonly used as indicators of the health and nutrition status of plants. Nowadays, the most popular method to quantify plant symptoms is based on visual estimations, consisting on evaluations that raters give based on their observation of plant symptoms; however, this method is inaccurate and imprecise because of its obvious subjectivity. Computational Vision has been employed in plant symptom quantification because of its accuracy and precision. Nevertheless, the systems developed so far lack in-situ, real-time and multi-symptom analysis. There exist methods to obtain information about the health and nutritional status of plants based on reflectance and chlorophyll fluorescence, but they use expensive equipment and are frequently destructive. Therefore, systems able of quantifying plant symptoms overcoming the aforementioned disadvantages that can serve as indicators of health and nutrition in plants are desirable. This paper reports an FPGA-based smart sensor able to perform non-destructive, real-time and in-situ analysis of leaf images to quantify multiple symptoms presented by diseased and malnourished plants; this system can serve as indicator of the health and nutrition in plants. The effectiveness of the proposed smart-sensor was successfully tested by analyzing diseased and malnourished plants.

FPGA-based Fused Smart Sensor for Real-Time Plant-Transpiration Dynamic Estimation

Plant transpiration is considered one of the most important physiological functions because it constitutes the plants evolving adaptation to exchange moisture with a dry atmosphere which can dehydrate or eventually kill the plant. Due to the importance of transpiration, accurate measurement methods are required; therefore, a smart sensor that fuses five primary sensors is proposed which can measure air temperature, leaf temperature, air relative humidity, plant out relative humidity and ambient light. A field programmable gate array based unit is used to perform signal processing algorithms as average decimation and infinite impulse response filters to the primary sensor readings in order to reduce the signal noise and improve its quality. Once the primary sensor readings are filtered, transpiration dynamics such as: transpiration, stomatal conductance, leaf-air-temperature-difference and vapor pressure deficit are calculated in real time by the smart sensor. This permits the user to observe different primary and calculated measurements at the same time and the relationship between these which is very useful in precision agriculture in the detection of abnormal conditions. Finally, transpiration related stress conditions can be detected in real time because of the use of online processing and embedded communications capabilities.

FPGA based multiple-channel vibration analyzer embedded system for industrial applications in automatic failure detection

Machine monitoring is one of the major concerns in modern industry in order to guarantee the overall efficiency during the production process. Several monitoring techniques for machinery failure detection have been developed, being vibration analysis one of the most important techniques. The typical equipment used for vibration analysis is a general purpose single channel spectrum analyzer that most of the cases is not well suited for the specific task and lacks from the capability of simultaneous multiple channel analysis. The contribution of this work is to present the development of a low-cost FPGA based 3-axis simultaneous vibration analyzer for embedded machinery monitoring with the novelty of a post-processing stage that can be designed and implemented into the same FPGA for automatic online detection of specific machinery failures. Two cases of study are presented to show the development performance and capabilities of the system where specific post-processing units are designed. From the results it can be seen that several mechanical failures can be automatically detected by reconfiguring the postprocessing algorithm, embedded in the system.

Embedded FPGA based induction motor monitoring system with speed drive fed using multiple wavelet analysis

Preventive maintenance on machinery is one of the major concerns in industry. Induction motors represent 85% of the total power consumption in the world and several faults related to these motors that increase the power consumption are not easily detected; therefore, periodic fault monitoring is mandatory. There are several techniques for fault detection in induction motors being current analysis the most popular among others. This techniques are focused on the analysis of induction motors that are directly fed by the line and do not consider the induced effects due to speed drives. The novelty of this research is the proposal of a new technique, based on discrete wavelet transform for transient analysis at the motor start, for broken rotor bar detection on induction motors fed in both: directly and with speed drive. The contribution of the present work is the development of a field programmable gate array embedded system implementation of a broken rotor bar on-line monitoring system for a low cost embedded system approach. Results from experimentation show the overall system performance. It is demonstrated that the proposed methodology is efficient to detect the motor faults when directly or speed drive fed.

FPGA-Based Smart Sensor for Drought Stress Detection in Tomato Plants Using Novel Physiological Variables and Discrete Wavelet Transform

Soil drought represents one of the most dangerous stresses for plants. It impacts the yield and quality of crops, and if it remains undetected for a long time, the entire crop could be lost. However, for some plants a certain amount of drought stress improves specific characteristics. In such cases, a device capable of detecting and quantifying the impact of drought stress in plants is desirable. This article focuses on testing if the monitoring of physiological process through a gas exchange methodology provides enough information to detect drought stress conditions in plants. The experiment consists of using a set of smart sensors based on Field Programmable Gate Arrays (FPGAs) to monitor a group of plants under controlled drought conditions. The main objective was to use different digital signal processing techniques such as the Discrete Wavelet Transform (DWT) to explore the response of plant physiological processes to drought. Also, an index-based methodology was utilized to compensate the spatial variation inside the greenhouse. As a result, differences between treatments were determined to be independent of climate variations inside the greenhouse. Finally, after using the DWT as digital filter, results demonstrated that the proposed system is capable to reject high frequency noise and to detect drought conditions.

Characteristics of Mycotoxin Analysis Tools for Tomorrow

Aflatoxins difuranocoumarin derivatives are produced by fungi Aspergillus flavus, Aspergil‐ lus parasiticus and Aspergillus nomius [1] and they are part of the group of mycotoxins. From the twenty metabolites that have been formed endogenously in animals, aflatoxins B1, B2, G1 and G2 (AFB1, AFB2, AFG1 and AFG2) are the most common and the most toxic. The names of aflatoxins B1, B2, G1, and G2 are based on their florescence characteristics. Aflatoxin B1 and B2 show strong blue fluorescence under UV light, whereas aflatoxins G1 and G2 exhibit greenish yellow fluorescence [2]. All the aflatoxins have been classified as carcinogenic com‐ pounds for humans, but AFB1 has been tagged as the most dangerous, highly toxic, immu‐ nosuppressive, mutagenic, and teratogenic compound and its effects have been identified as well. Also, malabsorption syndrome and reduction in bone strength may occur due to AFs consumption. Aflatoxins not only have adverse effects on human health but also cause seri‐ ous economic losses when tons of foods have to be dropped or destroyed for being contami‐ nated with AFs.

Novel ST-MUSIC-based spectral analysis for detection of ULF geomagnetic signals anomalies associated with seismic events in Mexico

Recently, the analysis of ultra-low-frequency (ULF) geomagnetic signals in order to detect seismic anomalies has been reported in several works. Yet, they, although having promising results, present problems for their detection since these anomalies are generally too much weak and embedded in high noise levels. In this work, a short-time multiple signal classification (ST-MUSIC), which is a technique with high-frequency resolution and noise immunity, is proposed for the detection of seismic anomalies in the ULF geomagnetic signals. Besides, the energy (E) of geomagnetic signals processed by ST-MUSIC is also presented as a complementary parameter to measure the fluctuations between seismic activity and seismic calm period. The usefulness and effectiveness of the proposal are demonstrated through the analysis of a synthetic signal and five real signals with earthquakes. The analysed ULF geomagnetic signals have been obtained using a tri-axial fluxgate magnetometer at the Juriquilla station, which is localized in Queretaro, Mexico (geographic coordinates: longitude 100.45° E and latitude 20.70° N). The results obtained show the detection of seismic perturbations before, during, and after the main shock, making the proposal a suitable tool for detecting seismic precursors.

Methods for Detection and Quantification of Aflatoxins

Mycotoxins are fungal toxic metabolites which naturally contaminate food and feed. aflatoxins (AFs), a kind of mycotoxins, are the main toxic secondary metabolites of some Aspergillus moulds such as Aspergillus flavus, Aspergillus parasiticus and the rare Aspergillus nomius (Ali et al., 2005, Alcaide-Molina et al., 2009). Such toxins can be separated into aflatoxins B1, B2, G1, B2a and G2a. Its order of toxicity is B1 > G1 > B2 > G2. Letters ‘B’ and ‘G’ refer to its blue and green fluorescence colors produced by these compounds under UV light. Numbers 1 and 2 indicate major and minor compounds, respectively (Weidenborner, 2001; Hussein & Brasel, 2001). A. flavus only produces B aflatoxins, while A. parasiticus and A. nomius also produce G aflatoxins (Alcaide-Molina et al., 2009). Aflatoxins are produced on various grains and nuts, e.g., corn, sorghum, cottonseed, peanuts, pistachio nuts, copra, cereals, fruits, oilseeds, dried fruits, cocoa, spices and beer in the field and during storage. AFs occur mainly in hot and humid regions where high temperature and humidity are optimal for moulds growth and toxins production (Ventura et al., 2004; Zollner & Mayer-Helm, 2006). Its presence is enhanced by factors as stress or damage to the crop due to drought before harvest, insect activity, soil type and inadequate storage conditions (Alcaide-Molina et al., 2009). Aflatoxins, when ingested, inhaled or adsorbed through the skin, have carcinogenic, hepatotoxic, teratogenic and mutagenic effects in human and animals (rats, ferrets, ducks, trout, dogs, turkeys, cattle and pigs) (Anwar-Ul_Haq & Iqbal, 2004) even at very small concentrations. When aflatoxins B1 is ingested by cows, it is transformed into its hydroxylated product, AFs M1 and M2. Such aflatoxins is secreted in the milk and is relatively stable during milk pasteurization, storage, and preparation of various dairy products (Stroka & Anklam, 2002). Among the more than 300 known mycotoxins, aflatoxins represent the main threat worldwide. After 1975 there has been an increased concern about the possibility of the presence of carcinogenic mold metabolites, particularly aflatoxins in food and animal feed products. Although aflatoxins are regulated in more than 80 countries, their legislation is not yet completely harmonized at the international level (Cucci et al., 2007). Several