Cantilever signature of tip detachment during contact resonance AFM

Contact resonance atomic force microscopy, piezoresponse force microscopy, and electrochemical strain microscopy are atomic force microscopy modes in which the cantilever is held in contact with the sample at a constant average force while monitoring the cantilever motion under the influence of a small, superimposed vibrational signal. Though these modes depend on permanent contact, there is a lack of detailed analysis on how the cantilever motion evolves when this essential condition is violated. This is not an uncommon occurrence since higher operating amplitudes tend to yield better signal-to-noise ratio, so users may inadvertently reduce their experimental accuracy by inducing tip–sample detachment in an effort to improve their measurements. We shed light on this issue by deliberately pushing both our experimental equipment and numerical simulations to the point of tip–sample detachment to explore cantilever dynamics during a useful and observable threshold feature in the measured response. Numerical simulations of the analytical model allow for extended insight into cantilever dynamics such as full-length deflection and slope behavior, which can be challenging or unobtainable in a standard equipment configuration. With such tools, we are able to determine the cantilever motion during detachment and connect the qualitative and quantitative behavior to experimental features.

A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector

The increasing availability of data, gathered by sensors and intelligent machines, is changing the way decisions are made in the manufacturing sector. In particular, based on predictive approach and facilitated by the nowadays growing capabilities of hardware, cloud-based solutions, and new learning approaches, maintenance can be scheduled—over cell engagement and resource monitoring—when required, for minimizing (or managing) unexpected equipment failures, improving uptime through less aggressive maintenance schedules, shortening unplanned downtime, reducing excess (direct and indirect) cost, reducing long-term damage to machines and processes, and improve safety plans. With access to increased levels of data (and over learning mechanisms), companies have the capability to conduct statistical tests using machine learning algorithms, in order to uncover root causes of problems previously unknown. This study analyses the maturity level and contributions of machine learning methods for predictive maintenance. An upward trend in publications for predictive maintenance using machine learning techniques was identified with the USA and China leading. A mapping study—steady set until early 2019 data—was employed as a formal and well-structured method to synthesize material and to report on pervasive areas of research. Type of equipment, sensors, and data are mapped to properly assist new researchers in positioning new research activities in the domain of smart maintenance. Hence, in this paper, we focus on data-driven methods for predictive maintenance (PdM) with a comprehensive survey on applications and methods until, for the sake of commenting on stable proposal, 2019 (early included). An equal repartition between evaluation and validation studies was identified, this being a symptom of an immature but growing research area. In addition, the type of contribution is mainly in the form of models and methodologies. Vibrational signal was marked as the most used data set for diagnosis in manufacturing machinery monitoring; furthermore, supervised learning is reported as the most used predictive approach (ensemble learning is growing fast). Neural networks, followed by random forests and support vector machines, were identified as the most applied methods encompassing 40% of publications, of which 67% related to deep neural network with long short-term memory predominance. Notwithstanding, there is no robust approach (no one reported optimal performance over different case tests) that works best for every problem. We finally conclude the research in this area is moving fast to gather a separate focused analysis over the last two years (whenever stable implementations will appear).

Vibrational Spectroscopic Investigation of Blood Plasma and Serum by Drop Coating Deposition for Clinical Application

In recent decades, vibrational spectroscopic methods such as Raman and FT-IR spectroscopy are widely applied to investigate plasma and serum samples. These methods are combined with drop coating deposition techniques to pre-concentrate the biomolecules in the dried droplet to improve the detected vibrational signal. However, most often encountered challenge is the inhomogeneous redistribution of biomolecules due to the coffee-ring effect. In this study, the variation in biomolecule distribution within the dried-sample droplet has been investigated using Raman and FT-IR spectroscopy and fluorescence lifetime imaging method. The plasma-sample from healthy donors were investigated to show the spectral differences between the inner and outer-ring region of the dried-sample droplet. Further, the preferred location of deposition of the most abundant protein albumin in the blood during the drying process of the plasma has been illustrated by using deuterated albumin. Subsequently, two patients with different cardiac-related diseases were investigated exemplarily to illustrate the variation in the pattern of plasma and serum biomolecule distribution during the drying process and its impact on patient-stratification. The study shows that a uniform sampling position of the droplet, both at the inner and the outer ring, is necessary for thorough clinical characterization of the patient’s plasma and serum sample using vibrational spectroscopy.

WaveNet based Autoencoder Model: Vibration Analysis on Centrifugal Pump for Degradation Estimation.

Centrifugal pumps are versatile and have been used in a wide range of applications such as agricultural services, wastewater services, and other industrial services. The mechanism behind the pump is converting rotational kinetic energy to induce flow or raise pressure of liquid. Boiler feedewater pump (BFP) is an important piece of equipment in a thermal power generation plant. Generally, the cost of the pump itself only account less than 20% of its life cost and about 30% - 35% of the life cost spend on pump operation and maintenance. Therefore, it is important to understand the degradation status of the pumping system for optimizing the operational procedures and maintenance schedule to reduce the cost. Traditionally, engineers evaluate the performance and/or find faults by observing the vibrational signal on the pump, specifically, looking at the power spectrum density of the vibrational signal measured on different locations of the pump. However, such vibration analysis requires substantial domain knowledge and experience to accommodate all the variables caused by various conditions like different models, sizes in different plants, units and facilities. Often Vibration Analyst have to bin the vibration signal according to a predetermined frequency bins and potentially removing useful markers about vibration health. This paper presents a novel way of conducting vibration analysis on pumps to determine the degradation trend, without requiring expert domain knowledge by extracting useful information using a WaveNet based autoencoder on the historical vibration data. WaveNet is known for processing raw audio data and building generative models. Unlike recurrent neural network (RNN), WaveNet is capable of handling much longer sequential data, which is very suitable for high frequency signals like sound and vibration signals. The autoencoder model extract essential information for reconstructing the input data. The embeddings from the autoencoders can represent the characteristics of the input data. Combining the two techniques, we were able to compress the vibration data 12x and extract the embeddings from raw vibration data and use them to estimate the degradation status of pumps. We pre-selected a collection of vibration data from pumps under “normal” condition. The degradation trend is estimated by computing the distance of the embeddings from “normal” data to new inputs. Such model provides additional information on pump condition vis-a-vis vibration data with no prior domain knowledge. This technique can assist decision making and reduce costs from improper operation and maintenance.

Brown marmorated stink bug overwintering aggregations are not regulated through vibrational signals during autumn dispersal

The brown marmorated stink bug, Halyomorpha halys (Heteroptera: Pentatomidae), is regarded as one of the world's most pernicious invasive pest species, as it feeds on a wide range of economically important crops. During the autumn dispersal period, H. halys ultimately moves to potential overwintering sites, such as human-made structures or trees where it will alight and seek out a final overwintering location, often aggregating with other adults. The cues used during this process are unknown, but may involve vibrational signals. We evaluated whether vibrational signals regulate cluster aggregation in H. haly s in overwintering site selection. We collected acoustic data for six weeks during the autumn dispersal period and used it to quantify movement and detect vibrational communication of individuals colonizing overwintering shelters. Both movement and vibrational signal production increased after the second week, reaching their maxima in week four, before decaying again. We found that only males produced vibrations in this context, yet there was no correlation between movement and vibrational signals , which was confirmed through playback experiments. The cues regulating the formation of aggregations remain largely unknown, but vibrations may indicate group size.

Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity

The health condition monitoring of rotating machinery can avoid the disastrous failure and guarantee the safe operation. The vibration-based fault diagnosis shows the most attractive character for fault diagnosis of rotating machinery (FDRM). Recently, Lempel-Ziv complexity (LZC) has been investigated as an effective tool for FDRM. However, the LZC only performs single-scale analysis, which is not suitable to extract the fault features embedded in vibrational signal over multiple scales. In this paper, a novel complexity analysis algorithm, called hierarchical Lempel-Ziv complexity (HLZC), was developed to extract the fault characteristics of rotating machinery. The proposed HLZC method considers the fault information hidden in both low-frequency and high-frequency components, resulting in a more accurate fault feature extraction. The superiority of the proposed HLZC method in detecting the periodical impulses was validated by using simulated signals. Meanwhile, two experimental signals were utilized to prove the effectiveness of the proposed HLZC method in extracting fault information. Results show that the proposed HLZC method had the best diagnosing performance compared with LZC and multi-scale Lempel-Ziv complexity methods.

Plasmonic nanocavity enhanced vibration of graphene by a radially polarized optical field

The combination of 2D materials and surface plasmon can produce some novel optical phenomena that have attracted much attention. Illuminated by light with different polarization states, the field distribution around the plasmonic structure can control the light-matter interaction. The interaction between graphene and light can be strongly enhanced by employing radially polarized beams in a nanocavity. Here, we study the selectively enhanced vibration of graphene in a coupled plasmonic gold nanocavity with a radially polarized optical field, and the coupling and enhancing mechanisms are investigated both experimentally and numerically. By focusing a radially polarized beam, a high z component of a localized near field in the nanocavity is provided to strongly enhance the interaction between graphene and light, which can be used to enhance the vibrational signal of the interlayer. For the in-plane vibration of graphene, a similar enhancement is obtained with a linearly and radially polarized optical field. A plasmonic nanocavity is used to enhance the vibration of graphene, which provides potential applications in studying the out-of-plane vibration mode and exploring the mechanism of the interlayer coupling of 2D materials.