There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Economic Control of Quality of Manufactured Product.

Many scientific papers have been written concerning gas sensors for different sensor applications using several sensing principles. This review focuses on sensors and sensor systems for gaseous ammonia. Apart from its natural origin, there are many sources of ammonia, like the chemical industry or intensive life-stock. The survey that we present here treats different application areas for ammonia sensors or measurement systems and different techniques available for making selective ammonia sensing devices. When very low concentrations are to be measured, e.g. less than 2 ppb for environmental monitoring and 50 ppb for diagnostic breath analysis, solid-state ammonia sensors are not sensitive enough. In addition, they lack the required selectivity to other gasses that are often available in much higher concentrations. Optical methods that make use of lasers are often expensive and large. Indirect measurement principles have been described in literature that seems very suited as ammonia sensing devices. Such systems are suited for miniaturization and integration to make them suitable for measuring in the small gas volumes that are normally available in medical applications like diagnostic breath analysis equipment.

/pdf/ammonia-sensors-and-their-applications-a-review-35hg34lbf3.pdf

Ammonia sensors and their applications - a review

2.2. New Approaches 707 2.2.1. Optical Sensor Systems 707 2.2.2. Mass Spectrometry 708 2.2.3. Ion Mobility Spectrometry 708 2.2.4. Gas Chromatography 709 2.2.5. Infrared Spectroscopy 709 2.2.6. Use of Substance-Class-Specific Sensors 709 2.3. Combined Technologies 710 3. Companies 710 4. Application Areas 710 4.1. Food and Beverage 712 4.2. Environmental Monitoring 715 4.3. Disease Diagnosis 716 5. Research and Development Trends 718 5.1. Sample Handling 719 5.2. Filters and Analyte Gas Separation 719 5.3. Data Evaluation 720 6. Conclusion 721 7. References 722

/pdf/electronic-nose-current-status-and-future-trends-475b22mugv.pdf

Electronic nose: current status and future trends.

Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

Incipient fault detection and diagnosis based on Kullback-Leibler divergence using principal component analysis

The rapid development of photovoltaic (PV) technology and the growing number and size of PV power plants require increasingly efficient and intelligent health monitoring strategies to ensure reliable operation and high energy availability. Among the various techniques, Artificial Neural Network (ANN) has exhibited the functional capacity to perform the identification and classification of PV faults. In the present review, a systematic study on the application of ANN and hybridized ANN models for PV fault detection and diagnosis (FDD) is conducted. For each application, the targeted PV faults, the detectable faults, the type and amount of data used, the model configuration and the FDD performance are extracted, and analyzed. The main trends, challenges and prospects for the application of ANN for PV FDD are extracted and presented.

https://hal-centralesupelec.archives-ouvertes.fr/hal-03087601/document

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

https://hal-centralesupelec.archives-ouvertes.fr/hal-01970593/document

PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system

This research deals with the discrimination between conditions of faults in rolling element bearings based on a global spectral analysis This global spectral analysis allows to obtain spectral features with significant discriminatory power These features are extracted from the envelope spectra of vibration signals without prior knowledge of the bearings specific parameters and the characteristic frequencies These extracted spectral features will then be the global spectral signature produced by the bearing faults Since the signature produced by the faults in bearing balls is very weak, and hard to be detected and identified, this paper proposes the linear discriminant analysis as part of the global spectral analysis method in order to improve the diagnosis of ball faults The application on experimental vibration data acquired from bearings containing different types of faults with different small sizes shows the proficiency of the overall method The Bhattacharyya distance is used to confirm the efficiency of the obtained results

Claude Delpha

Papers

Incipient fault detection and diagnosis based on Kullback-Leibler divergence using principal component analysis

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system

Improved Fault Diagnosis of Ball Bearings Based on the Global Spectrum of Vibration Signals

An optimal fault detection threshold for early detection using Kullback-Leibler Divergence for unknown distribution data