Development of Material Characterization Techniques using Novel Nanoindentation Approaches on Hard and Soft Materials used in MEMS

Investigating and modeling the mechanical properties of materials is important for many applications. The most common technique used for mechanical characterization of materials is called nanoindentation. The currently available tools utilized in order to perform nanoindentation have their limitations in terms of sensitivities in force and displacement for a broad range of material properties. When it comes to investigation of soft materials, these limitations might be more detrimental. In this dissertation work, novel nanoindentation techniques have been developed with a multi-probe scanning force microscopy (SPM) system in order to ease the major problems encountered with standard Atomic Force Microscopy (AFM) or nanoindentation systems. Tuning forks are used as probes during nanoindentation. By using the newly developed nanoindentation techniques for quasi-static nanoindentation experiments, the force information is extracted through the displacement of the indenter probe measured by a second probe with ultraresolution. For dynamic nanoindentation, frequency modulation techniques have been used to extract force information from a single indenter tuningfork probe. Thanks to the high quality of resonance (Q factor) of tuning fork probes, force measurements can be performed with an ultra high resolution. The accurate measurements of material properties on soft materials is used in characterization of microfabricated pillar sensors which can be used in measuring nN level of cell traction forces in a biomedical application. The techniques developed in this research also enable the system as an ultra-sensitive force sensor to apply nN scale lateral and vertical loads on microfabricated structures or biological specimens

A Study of recent classification algorithms and a novel approach for biosignal data classification

Analyzing and understanding human biosignals have been important research areas that have many practical applications in everyday life. For example, Brain Computer Interface is a research area that studies the connection between the human brain and external systems by processing and learning the brain signals called Electroencephalography (EEG) signals. Similarly, various assistive robotics applications are being developed to interpret eye or muscle signals in humans in order to provide control inputs for external devices. The efficiency for all of these applications depends heavily on being able to process and classify human biosignals. Therefore many techniques from Signal Processing and Machine Learning fields are applied in order to understand human biosignals better and increase the efficiency and success of these applications. This thesis proposes a new classifier for biosignal data classification utilizing Particle Swarm Optimization Clustering and Radial Basis Function Networks (RBFN). The performance of the proposed classifier together with several variations in the technique is analyzed by utilizing comparisons with the state of the art classifiers such as Fuzzy Functions Support Vector Machines (FFSVM), Improved Fuzzy Functions Support Vector Machines (IFFSVM). These classifiers are implemented on the classification of same biological signals in order to evaluate the proposed technique. Several clustering algorithms, which are used in these classifiers, such as K-means, Fuzzy c-means, and Particle Swarm Optimization (PSO), are studied and compared with each other based on clustering abilities. The effects of the analyzed clustering algorithms in the performance of Radial Basis Functions Networks classifier are investigated. Strengths and weaknesses are analyzed on various standard and EEG datasets. Results show that the proposed classifier that combines PSO clustering with RBFN classifier can reach or exceed the performance of these state of the art classifiers. Finally, the proposed classification technique is applied to a real-time system application where a mobile robot is controlled based on person\u27s EEG signal

A Sensor Fusion Method Using Transfer Learning Models for Equipment Condition Monitoring

Sensor fusion is becoming increasingly popular in condition monitoring. Many studies rely on a fusion-level strategy to enable the most effective decision-making and improve classification accuracy. Most studies rely on feature-level fusion with a custom-built deep learning architecture. However, this may limit the ability to use the widely available pre-trained deep learning architectures available to users today. This study proposes a new method for sensor fusion based on concepts inspired by image fusion. The method enables the fusion of multiple and heterogeneous sensors in the time-frequency domain by fusing spectrogram images. The method’s effectiveness is tested with transfer learning (TL) techniques on four different pre-trained convolutional neural network (CNN) based model architectures using an original test environment and data acquisition system. The results show that the proposed sensor fusion technique effectively classifies device faults and the pre-trained TL models enrich the model training capabilities

A Deep-Learning-Based Multi-Modal Sensor Fusion Approach for Detection of Equipment Faults

Condition monitoring is a part of the predictive maintenance approach applied to detect and prevent unexpected equipment failures by monitoring machine conditions. Early detection of equipment failures in industrial systems can greatly reduce scrap and financial losses. Developed sensor data acquisition technologies allow for digitally generating and storing many types of sensor data. Data-driven computational models allow the extraction of information about the machine’s state from acquired sensor data. The outstanding generalization capabilities of deep learning models have enabled them to play a significant role as a data-driven computational fault model in equipment condition monitoring. A challenge of fault detection applications is that single-sensor data can be insufficient in performance to detect equipment anomalies. Furthermore, data in different domains can reveal more prominent features depending on the fault type, but may not always be obvious. To address this issue, this paper proposes a multi-modal sensor fusion-based deep learning model to detect equipment faults by fusing information not only from different sensors but also from different signal domains. The effectiveness of the model’s fault detection capability is shown by utilizing the most commonly encountered equipment types in the industry, such as electric motors. Two different sensor types’ raw time domain and frequency domain data are utilized. The raw data from the vibration and current sensors are transformed into time-frequency images using short-time Fourier transform (STFT). Then, time-frequency images and raw time series data were supplied to the designed deep learning model to detect failures. The results showed that the fusion of multi-modal sensor data using the proposed model can be advantageous in equipment fault detection

A Predictive Maintenance System Design and Implementation for Intelligent Manufacturing

The importance of predictive maintenance (PdM) programs has been recognized across many industries. Seamless integration of the PdM program into today’s manufacturing execution systems requires a scalable and generic system design and a set of key performance indicators (KPIs) to make condition monitoring and PdM activities more effective. In this study, a new PdM system and its implementation are presented. KPIs and metrics are proposed and implemented during the design to enhance the system and the PdM performance monitoring needs. The proposed system has been tested in two independent use cases (autonomous transfer vehicle and electric motor) for condition monitoring applications to detect incipient equipment faults or operational anomalies. Machine learning-based data augmentation tools and models are introduced and automated with state-of-the-art AutoML and workflow automation technologies to increase the system’s data collection and data-driven fault classification performance

A Predictive Maintenance System Design and Implementation for Intelligent Manufacturing

The importance of predictive maintenance (PdM) programs has been recognized across many industries. Seamless integration of the PdM program into today’s manufacturing execution systems requires a scalable and generic system design and a set of key performance indicators (KPIs) to make condition monitoring and PdM activities more effective. In this study, a new PdM system and its implementation are presented. KPIs and metrics are proposed and implemented during the design to enhance the system and the PdM performance monitoring needs. The proposed system has been tested in two independent use cases (autonomous transfer vehicle and electric motor) for condition monitoring applications to detect incipient equipment faults or operational anomalies. Machine learning-based data augmentation tools and models are introduced and automated with state-of-the-art AutoML and workflow automation technologies to increase the system’s data collection and data-driven fault classification performance

Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence

Early fault detection and real-time condition monitoring systems have become quite significant for today’s modern industrial systems. In a high volume of manufacturing facilities, fleets of equipment are expected to operate uninterrupted for days or weeks. Any unplanned interruptions to equipment uptime could jeopardize manufacturers’ cycle time, capacity, and, most significantly, credibility for their customers. With the help of smart manufacturing technologies, companies have started to develop and integrate fault detection and classification systems where end-to-end constant monitoring of equipment is facilitated, and smart algorithms are adapted for the early generation of fault alarms and classification. This paper proposes a generic real-time fault diagnosis and condition monitoring system utilizing edge artificial intelligence (edge AI) and a data distributor open source middleware platform called FIWARE. The implemented system architecture is flexible and includes interfaces that can be easily expanded for various devices. This work demonstrates it for condition monitoring of autonomous transfer vehicle (ATV) equipment targeting a smart factory use case. The system is verified in a designated industrial model environment in a lab with a single ATV operation. The anomaly conditions of the ATV are diagnosed by a deep learning-based fault diagnosis method performed in the Edge AI unit, and the results are transferred to the data storage via a data pipeline setup. The proposed system’s Edge AI solution for the ATV use case provides significant real-time performance. The network bandwidth requirement and total elapsed data transfer time have been reduced by 43 and 37 times, respectively. The proposed system successfully enables real-time monitoring of ATV fault conditions and expands to a fleet of equipment in a real manufacturing facility

Piezo1 regulates mechanotransductive release of ATP from human RBCs

Piezo proteins (Piezo1 and Piezo2) are recently identified mechanically activated cation channels in eukaryotic cells and associated with physiological responses to touch, pressure, and stretch. In particular, human RBCs express Piezo1 on their membranes, and mutations of Piezo1 have been linked to hereditary xerocytosis. To date, however, physiological functions of Piezo1 on normal RBCs remain poorly understood. Here, we show that Piezo1 regulates mechanotransductive release of ATP from human RBCs by controlling the shear-induced calcium (Ca(2+)) influx. We find that, in human RBCs treated with Piezo1 inhibitors or having mutant Piezo1 channels, the amounts of shear-induced ATP release and Ca(2+) influx decrease significantly. Remarkably, a critical extracellular Ca(2+) concentration is required to trigger significant ATP release, but membrane-associated ATP pools in RBCs also contribute to the release of ATP. Our results show how Piezo1 channels are likely to function in normal RBCs and suggest a previously unidentified mechanotransductive pathway in ATP release. Thus, we anticipate that the study will impact broadly on the research of red cells, cellular mechanosensing, and clinical studies related to red cell disorders and vascular disease