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Before skyscrapers forever transformed the landscape of the modern metropolis, the conveyance that made them possible had to be created. Invented in New York in the 1850s, the elevator became an urban fact of life on both sides of the Atlantic by the early twentieth century. While it may at first glance seem a modest innovation, it had wide-ranging effects, from fundamentally restructuring building design to reinforcing social class hierarchies by moving luxury apartments to upper levels, previously the domain of the lower classes. The cramped elevator cabin itself served as a reflection of life in modern growing cities, as a space of simultaneous intimacy and anonymity, constantly in motion.In this elegant and fascinating book, Andreas Bernard explores how the appearance of this new element changed notions of verticality and urban space. Transforming such landmarks as the Waldorf-Astoria and Ritz Tower in New York, he traces how the elevator quickly took hold in large American cities while gaining much slower acceptance in European cities like Paris and Berlin. Combining technological and architectural history with the literary and cinematic, Bernard opens up new ways of looking at the elevator--as a secular confessional when stalled between floors or as a recurring space in which couples fall in love. Rising upwards through modernity,Liftedtakes the reader on a compelling ride through the history of the elevator.Andreas Bernardis editor ofSuddeutsche Zeitung, Germany's largest daily newspaper. He received his Ph.D. in Cultural Sciences from the Bauhaus University Weimar, and teaches cultural studies in Berlin and Lucerne, Switzerland.

The rapid development of urbanization has led to a continuous rise in number of elevators. This has led to elevator failures from time to time. At present, although there are some studies on elevator fault diagnosis, they are more or less limited by the lack of data to make the research more superficial. For such complex special equipment as elevator, it is difficult to obtain reliable and sufficient data to train the fault diagnosis model. To address this issue, this paper first establishes a numerical model of vertical vibration for elevators with three degrees of freedom. The obtained motion equations are then used as constraints to acquire simulated vibration data through PINNs. Next, the proposed e-RGCN is employed for elevator fault diagnosis. Finally, experimental validation shows that the fault diagnosis accuracy with the participation of digital twins exceeds 90%, and the accuracy of the proposed model reaches 96.61%, significantly higher than that of other comparative models.

This article proposes a method for diagnosing the main systems of bucket elevators in order to ensure their reliable operation. This method employs diagnostic indices of vibration velocity and vibration acceleration, which were deemed useful based on tests performed on four bucket elevators operating in a research laboratory and in a power plant. This article also analyzes other indicators, such as the coefficient of variation, skewness, kurtosis, crest factor, and quantile peak factor, and demonstrates the usefulness of kurtosis for diagnostic evaluation. Additionally, it proposes using the quantile peak factor as an alternative to the crest factor. This study estimates the statistical distributions of diagnostic signals and presents the results in the form of histograms. This is followed by the detection of outliers in all measurement series. Based on the results of the performed tests and their analysis, recommendations are made for diagnosing bucket elevators.

The ageing of elevator travelling cables results in the breakage of inner copper strands, leading to communication and control faults in the elevator system. In this paper, a travelling cable state evaluation method based on time-frequency transformation and a deep learning fitting method is proposed. The cable diagnosis is based on the transmission line theory and finite element simulation results, which indicate that the number of broken strands of copper wires in twisted cables is positively related to the amplitude of fluctuation in the cable’s transmission spectrum. To evaluate this fluctuation with low cost and high accuracy, we acquired the 500 Msps time-domain signal after a square wave with different periods was transmitted through the detected cable; the transmission in base frequency and harmonics is calculated and combined into the total transmission spectrum. A deep learning model with a two-layer 1-D CNN and squeeze-excitation channel attention is utilized to fit the spectrum data, and cross-entropy is applied to estimate the departure between the fitting results and the experimental data, which serves as the cable’s broken-state index. Experiments demonstrate that the proposed method is able to detect minor cable faults such as one or two copper strands broken and could distinguish different broken states with a sensitivity of 16.42 ± 1.39 per break strand.

Elevators have become an integral part of modern buildings, and technological advances have enabled the monitoring of their operational status through sensor technology. In response to the development of the elevator industry and the need for practical elevator operation risk evaluation, this paper proposes an elevator risk evaluation method based on fuzzy theory and machine learning methods. The method begins by establishing an elevator operation risk evaluation index system. The traditional fuzzy comprehensive evaluation method is then employed to evaluate the risk levels of the 50 elevators studied. The collected index data and labels (fuzzy comprehensive evaluation results) are used as inputs to train the support vector machine (SVM) model. To optimize the SVM model, the maximum information coefficient method, enhanced by the correlation-based feature selection (MIC-CFS) method, is employed to select features for the index input to the SVM model. The improved gray wolf algorithm (IGWO) method optimizes the SVM. Finally, the model’s performance is verified using new index data. The experimental results demonstrate that introducing machine learning methods for elevator risk evaluation saves time and effort while providing good accuracy compared to the traditional expert evaluation method. The optimization of the SVM model by IGWO and feature selection by the MIC-CFS method results in a more concise SVM model that converges faster during training, exhibits better stability, and achieves higher accuracy.

The resistive leakage current caused by the insulation damage or aging of the elevator line is the main monitoring target of the electrical fire detector. However, due to the influence of the capacitance component of the elevator power supply cable and the motor to the ground, the detector is prone to false alarms. In this paper, the fast Fourier transform is used to analyze the phase of the residual current and the neutral line voltage, and the detection and protection methods of the slowly varying resistance residual current and the abrupt resistance residual current are proposed. The experimental results show that the method can effectively detect the elevator resistance residual current and improve the accuracy of the protector action.

As a high-frequency and essential type of special electromechanical equipment, a vertical elevator has a significant societal implication for their safe operation. The load-weighing module, serving as the core component for overload warning, is susceptible to precision degradation due to the nonlinear deformation of rubber buffers installed at the base of the elevator car. This deformation arises from the coupled effects of environmental factors such as temperature, humidity, and material aging, leading to potential safety risks including missed overload alarms and false empty status detections. To address the issue of accuracy deterioration in elevator load-weighing systems, this study proposes an online self-calibration method based on multimodal information fusion. A reference detection model is first constructed to map the relationship between applied load and the corresponding relative compression of the rubber buffers. Subsequently, displacement data from a draw-wire sensor are integrated with target detection model outputs, enabling real-time extraction of dynamic rubber buffers’ deformation characteristics under empty conditions. Based on the above, a displacement-based compensation term is derived to enhance the accuracy of load estimation. This is further supported by a dynamic error compensation mechanism and an online computation framework, allowing the system to self-calibrate without manual intervention. The proposed approach eliminates the dependency on manual tuning inherent in traditional methods and forms a highly robust solution for load monitoring. Field experiments demonstrate the effectiveness of the proposed method and the stability of the prototype system. The results confirm that the synergistic integration of multimodal perception and adaptive calibration technologies effectively resolves the challenge of load-weighing precision degradation under complex operating conditions, offering a novel technical paradigm for elevator safety monitoring.

Elevators exemplify mechatronics by integrating mechanical, electrical, and software systems. This paper discusses a four-story tabletop elevator model developed to demonstrate mechatronics and automation concepts in engineering education. The system utilized an Arduino MEGA microcontroller, 3D-printed components, an integrated servo motor, and standard electronics to replicate commercial elevator logic. The physical design features a ball screw linear actuator for vertical motion. It replicates dual-door systems with one door on the moving car and fixed doors at each floor that open simultaneously upon arrival. Development included designing the physical model, prototyping control algorithms, and integrating hardware and software. The model successfully demonstrated key functions: automatic dual-door operation, safety interlocks, smooth inter-floor motion, responsive floor-selection buttons with LED feedback, and efficient routing algorithms prioritizing requests based on current direction and location. Performance testing confirmed that the model accurately replicates modern elevator behavior and serves as an effective educational tool.

Most commercial elevators for buildings exceeding four stories use a cable-driven traction system. Typically, a single traction machine operates by hoisting the main cable on a traction sheave, thus vertically transporting the elevator car through rotational motion of the sheave. This research introduces a groundbreaking advancement aimed at elevating loading capacity to an unprecedented 50 tons—the highest known in the world. The innovation involves the development of a twin traction system, wherein two traction machines collaborate to lift the elevator. This novel elevator system has demonstrated remarkable capabilities, showcasing the ability to transport up to 300 passengers in a single trip. The installation of this high-capacity elevator system has yielded substantial improvements in construction work efficiency and safety protocols, particularly in scenarios where cranes are traditionally used. The newly developed elevator could lift 50 tons of equipment 60 times a day, whereas the crane was limited to 8 times. The positive impact on labor is also noteworthy, with increased safety and health considerations, especially in adverse weather conditions. By eliminating the need for manual stair climbing, the well-being of the workforce is prioritized. Furthermore, the heightened productivity resulting from a significant reduction in wait times for conventional elevators is a key outcome of this transformative technology. This research not only unveils a groundbreaking twin traction system but also highlights its multifaceted features in enhancing efficiency, safety, and overall productivity in various industries.