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Performance Degradation in Solar Modules
Published in Bhavnesh Kumar, Bhanu Pratap, Vivek Shrivastava, Artificial Intelligence for Solar Photovoltaic Systems, 2023
Degradation is defined as the gradual deterioration of the characteristics of a component or of a system, which may affect its ability to operate within the limits of acceptability criteria and is greatly influenced by the operating conditions (Lannoy and Procaccia, 2005). According to the manufacturers, a PV module is claimed to be degraded when its power reaches a level below 80% of its initial power (Wohlgemuth et al., 2005b). The PV module’s performance can be degraded due to several factors such as temperature, humidity, irradiation, mechanical shock (Quintana et al., 2002; Wohlgemuth et al., 2005a; Osterwald and McMahon, 2009; Munoz et al., 2011). The main kinds of solar PV module degradation are corrosion, discoloration, delamination, and breaking or cracking cells. Moreover, the degradations identified in crystalline silicon modules (Wohlgemuth and Kurtz, 2011; Bosco, 2010) are cracked interconnection, breakage in cells, sign of corrosion, delamination and discoloration of encapsulant, broken protecting cover glass, failure of bypass diode, and failure of welded ribbon.
Philosophy of design
Published in Chanakya Arya, Design of Structural Elements, 2022
If maintenance were the major contributor to environmental degradation, then more durable design materials or additional protection measures could be employed. These would reduce the maintenance frequency and associated traffic delays. Corrosion of steel is the main cause of deterioration of concrete bridges requiring maintenance work in the UK. The onset of corrosion can be delayed, and its subsequent rate of progress reduced, by modifying design details and materials. For example, using better quality concrete with a lower water/cement ratio or the use of cement replacement materials such as pulverised fly ash (PFA) and ground granulated blast furnace slab (GGBS) will slow the rate of chloride ion ingress, increase the time to corrosion and reduce the number of maintenance treatments.The use of stainless steel instead of mild steel will prevent corrosion and eliminate maintenance work resulting from corrosion.Dosing the concrete with corrosion inhibitors can have a similar effect.Providing deep concrete covers to conventional steel reinforcement and embedding bars made of non-corrodible materials at shallower depths in the cover to control crack widths will increase the time to corrosion initiation (Arya, 1994; Arya et al., 1995).
Life Prediction of Device Based on Material’s Micro-Structure Evolution by Means of Computational Materials Science
Published in Lirong Cui, Ilia Frenkel, Anatoly Lisnianski, Stochastic Models in Reliability Engineering, 2020
Chunyu Teng, Cong Lin, Zhanyong Ren, Yun Fu, Xichang Wang
But the fact is that the microstructure of a material changes during the serving process and may have a decisive influence on the application behaviour of the device. Grain growth, crack growth, and corrosion, and so on are the main degradation phenomena of concern. Degradation can be controlled by design during construction or by inspection during operation and repair. The gradual development of degradation phenomena and the prescription of minimum design allowances (in terms of fatigue life and corrosion addition) suggest that there is ample time to control the safety during operation. However, this requires proper safety management with reliable organizations and personnel, as opposed to the quality of the ‘as-built’ structure achieved by adequate design criteria with respect to degradation. Moreover, a decision on the balance between design criteria and inspection and repair procedures to ensure adequate safety obviously have economic implications (Ayyub and Assakkaf, 2006).
A novel approach for deterioration and damage identification in building structures based on Stockwell-Transform and deep convolutional neural network
Published in Journal of Structural Integrity and Maintenance, 2022
Vahid Reza Gharehbaghi, Hashem Kalbkhani, Ehsan Noroozinejad Farsangi, T.Y. Yang, Andy Nguyen, Seyedali Mirjalili, Christian Málaga-Chuquitaype
Damage and deterioration are defined as degradation in a system’s performance due to changes in material, components, or structure connections. However, compared to damage, deterioration identification requires more accurate and reliable techniques (Monavari, 2019). Recently, several studies have focused on damage identification through variations in the structure responses. Nonetheless, while these surveys have illuminated numerous practical approaches in the sense of identification and localization of damage on simple components or bridges(Gillich et al., 2017; Sha et al., 2020; Jayasundara et al., 2020), fewer have been done to assess slight damages on building structures (Regier & Hoult, 2015). This topic is exacerbated in complex structures like buildings, where a strong correlation of responses is considered one of the challenging issues (Gharehbaghi et al., 2020). Deterioration recognition in complex systems requires more laborious efforts. Since not only ambient excitations lead to lower amplitudes in responses, but noise and operational effects, influence the performance of detection.
Dynamic maintenance model for a repairable multi-component system using deep reinforcement learning
Published in Quality Engineering, 2022
Nooshin Yousefi, Stamatis Tsianikas, David W. Coit
In this paper, we proposed a dynamic maintenance policy for a degrading multi-component system, where each component can be repaired individually within the system, and it is subject to a degradation process and damage from environmental shocks. The gamma process is used to model the degradation path of all the components. The total degradation of each component is computed by the summation of the internal degradation process and the damages from arrived shocks. If the total degradation of any component is greater than a predefined threshold, it is detected as failed. The system is inspected periodically at some specific intervals, and at each inspection, maintenance actions can be implemented on all the components based on their degradation level. In this paper, to overcome the limitation of the maintenance models in the literature, we consider the exact level of system degradation instead of discretizing the state space. The problem is formulated as a Markov decision process with an infinite number of states, and the Deep Q learning algorithm is used to solve the problem and find the best maintenance action dynamically. Deep Q learning is one of the most commonly used algorithms, which is a combination of reinforcement learning and deep learning to solve the problems with an infinite number of states or actions. Numerical examples are provided to indicate the capability of the proposed method in finding the best maintenance action of systems with different configurations.
Multi-dimensional Lévy processes with Lévy copulas for multiple dependent degradation processes in lifetime analysis
Published in Quality Engineering, 2020
Yu Shi, Qianmei Feng, Yin Shu, Yisha Xiang
In engineering applications, it is common to observe more than one degradation process in a component (e.g., multiple crack growth on a metal surface), or in a multi-component system where several components are subjected to degradation due to wear, aging, fatigue, corrosion, etc. Typically, these degradation processes are dependent due to the complicated internal mechanisms (e.g., mechanical, thermal, electrical, or chemical) and/or the exposure to the common external conditions (e.g., temperature, pressure, humidity, or vibration). The analysis of multiple dependent degradation processes is a challenging research problem in the reliability field. They are commonly described using multi-dimensional stochastic processes that can characterize the physical degradation processes.