Purpose
This paper aims to apply a methodology that is capable to classify and localize mechanical, electrical and plumbing (MEP) elements to assist facility managers. Furthermore, it assists in decreasing the technical complexity and sophistication of different systems to the facility management (FM) team.
Design/methodology/approach
This research exploits artificial intelligence (AI) in FM operations through proposing a new system that uses a deep learning pre-trained model for transfer learning. The model can identify new MEP elements through image classification with a deep convolutional neural network using a support vector machine (SVM) technique under supervised learning. Also, an expert system is developed and integrated with an Android application to the proposed system to identify the required maintenance for the identified elements. FM team can reach the identified assets with bluetooth tracker devices to perform the required maintenance.
Findings
The proposed system aids facility managers in their tasks and decreases the maintenance costs of facilities by maintaining, upgrading, operating assets cost-effectively using the proposed system.
Research limitations/implications
The paper considers three fire protection systems for proactive maintenance, where other structural or architectural systems can also significantly affect the level of service and cost expensive repairs and maintenance. Also, the proposed system relies on different platforms that required to be consolidated for facility technicians and managers end-users. Therefore, the authors will consider these limitations and expand the study as a case study in future work.
Originality/value
This paper assists in a proactive manner to decrease the lack of knowledge of the required maintenance to MEP elements that leads to a lower life cycle cost. These MEP elements have a big share in the operation and maintenance costs of building facilities.
SUMMARYThis paper studies the effect of joint flexibility on the dynamic performance of a serial spatial robot arm of rigid links. Three models are developed in this paper. The first and the third models are developed using the multibody dynamics approach, while the second using the classical robotics approach. A numerical algorithm and an experimental test-rig are developed to test the final model. The links' inertial parameters are estimated numerically. Empirical formulae with assumption models are used to estimate the flexibility coefficients. The simulation results show that the joint damping is a major source of inaccuracies, causing trajectory error without a proper feedback controller.
This paper focuses on embedded control of a hybrid powertrain concepts for mobile vehicle applications. Optimal robust control approach is used to develop a real-time energy management strategy. The main idea is to store the normally wasted mechanical regenerative energy in energy storage devices for later usage. The regenerative energy recovery opportunity exists in any condition where the speed of motion is in the opposite direction to the applied force or torque. This is the case when the vehicle is braking, decelerating, the motion is driven by gravitational force, or load driven. There are three main concepts for energy storing devices in hybrid vehicles: electric, hydraulic, and mechanical (flywheel). The real-time control challenge is to balance the system power demands from the engine and the hybrid storage device, without depleting the energy storage device or stalling the engine in any work cycle. In the worst-case scenario, only the engine is used and the hybrid system is completely disabled. A rule-based control algorithm is developed and is tuned for different work cycles and could be linked to a gain scheduling algorithm. A gain scheduling algorithm identifies the cycle being performed by the work machine and its position via GPS and maps both of them to the gains.
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