With the construction of automated docks, health monitoring technology as a parallel safety assurance technology for unmanned hoisting machinery has become a hot spot in the development of the industry. Hoisting machinery has a huge structure and numerous welded joints. The complexity and nonlinearity of the welded structure itself makes the structural failure parts random and difficult to arrange for monitoring sensors. In order to solve the problem of effectiveness and stability of the sensor arrangement method for monitoring the structure of hoisting machinery. Using the global and local search capabilities enhanced by the complementary search mechanism, a structural vibration monitoring sensor placement algorithm based on the harmony genetic algorithm is proposed. Firstly, the model is established for modal analysis to obtain the displacement matrix of each mode. Secondly, the optimal parameter combination is established through parameter comparison, and the random search mechanism is used to quickly search in the modal matrix to obtain the preliminary solution, and then the preliminary solution is genetically summed The mutation operation obtains the optimized solution, and the optimal solution is retained through repeated iterations to realize the decision of the vibration sensor layout of the crane structure monitoring. Combining the comparison test of harmony genetic algorithm, harmony search algorithm and genetic algorithm, the fitness of harmony genetic algorithm in X, Y and Z directions were 0.0045, 0.0084 and 0.0058, respectively, which were all optimal. And the average probability of deviating from the optimal path is 1.10%, 19.34%, and 54.43%, which are also optimal. Harmony genetic algorithm has the advantages of simplicity, fastness and strong global search ability, and can obtain better fitness value and better search stability.

To overcome the limitations of traditional monitoring methods, which are restricted to periodic inspections, this study proposes a real-time method for identifying metal corrosion damage patterns to monitor the condition of Q235 steel corrosion based on acoustic emission (AE). Firstly, AE technology was utilized to monitor the corrosion process of Q235 steel plates in simulated industrial marine environment in real-time. Wavelet packet energy spectrum coefficients, closely related to the damage mechanism, were extracted from the acquired signals. A feature matrix was then constructed using principal component analysis (PCA) to eliminate redundant information and enhance computational efficiency. The K-means clustering algorithm was then applied to classify the AE signals into three classifications: the signals of mode 1 correspond to bubble rupture, the signals of mode 2 to pit growth and expansion, and the signals of mode 3 to the detachment of corrosion products and oxide film rupture. A damage pattern recognition model based on a convolutional neural network (CNN) was developed, enabling the real-time recognition of other unknown AE signals generating during the corrosion process of Q235 steel, and it exhibited satisfactory performance in accurately identifying corrosion-related acoustic emission patterns.

Lifting machinery is a key facility for the development of modern industry. It’s not noly an important support for the construction of major national projects but also one of the core competitiveness of my country’s equipment manufacturing industry. With the development of large-scale, high-parameterization and intelligentization of lifting appliances, technologies such as safety monitoring and health monitoring have been gradually applied to industrial fields such as ports and metallurgy. In order to further develop the data-driven health management technology and engineering application of cranes, on the basis of elaborating the health management needs of cranes, the current status and development trends at home and abroad, we explored and proposed a technical framework for health management of cranes, and analyzed the lifting Key technologies such as machinery safety monitoring, health monitoring, health status diagnosis, failure trend prediction, and predictive maintenance decisions. Finally combined with a 200t casting crane in a steel plant, carried out safety monitoring and data-driven diagnosis, prediction and system visualization management. The results show that: the hoisting machinery health management system can monitor and monitor in real time and carry out early warning and forecasting to avoid the occurrence of unexpected accidents; based on predictive maintenance decisions, it helps enterprises to reasonably arrange maintenance cycles and maintenance methods to reduce the maintenance cost of the enterprise; reduce hoisting machinery downtime Time to enhance the effective value of assets; and enhance the level of enterprise information management.

Local damages such as microcracks and corrosion in crane steel structures often exhibit strong localization and weak mode shape perturbation characteristics. Especially when the damage scale is smaller than the modal wavelength, it cannot be simply treated as an overall damage issue for identification. In response to the diverse damage modes of crane structures and the need for dense sensor placement for local identification, a sensor optimization placement method based on multi-level damage feature fusion is proposed. Firstly, structural sub-regions are divided according to the main beam diaphragms, and sensors are arranged with boundary points as key measurement points. The displacement frequency response amplitude changes before and after damage are utilized to identify damage and eliminate insensitive measurement points to complete preliminary optimization. Secondly, a displacement frequency response amplitude change matrix is constructed, and the damage signal is enhanced through cross-frequency weighted superposition to form a damage identification vector, accurately locating the damage occurrence area. Furthermore, node-level correction is performed in candidate areas based on displacement flexibility difference values, and precise localization of damage points is achieved through priority sorting of flexibility differences. Simulation results show that under the condition where corrosion damage is set in units 20739, 20762, and 20785, the maximum point of the flexibility difference damage index is located in unit 20762, which coincides with the preset damage location, verifying the effectiveness of the hierarchical placement strategy from initial damage screening to precise localization.

In this paper, a fiber optic acoustic sensor with continuously adjustable membrane stress technology is proposed, in which the stress exerted on the membrane can be flexibly adjusted by employing the inverse piezoelectric effect of lead zirconate titanate (PZT) tubular pedestal. First, the response mechanism of stress on the membrane is investigated theoretically. Based on this principle, the effect of adjustable stress on the flat response region and sensitivity of a single mode-multimode-single mode (SMS) fiber optic acoustic sensor are studied experimentally. The experimental results reveal that the flat response region of the sensor extends from 250 Hz to 450 Hz following the continuous increase in the membrane stress by changing the positive direct current voltage applied on PZT from 0 V to 100 V; meanwhile, the sensitivity decreases gradually from 336.5 mV Pa⁻¹ to 162.7 mV Pa⁻¹. The results also verify that the average minimum detectable pressure of the sensor is maintained at 0.54 mPa Hz^−1/2 with a fluctuation of 0.08 mPa Hz^−1/2 during the tuning process of membrane stress, which provides a flexible method for performance and applicability improvement of membrane-based fiber optic acoustic sensors.