Abstract
With the development of manufacturing towards automation and intelligence, the field of intelligent operation and maintenance for grain and oil processing equipment has also received increasing attention and research. This article mainly outlines the current development status and challenges of equipment operation and maintenance in the grain and oil processing industry, analyzes the technological development status of equipment operation and maintenance systems in areas such as equipment fault diagnosis and predictive maintenance, and discusses the advantages and development directions of intelligent equipment operation and maintenance systems, aiming to provide important references for researchers in related fields.
Keywords: Grain and oil processing; Equipment operation and maintenance system; Fault prediction; Fault diagnosis
The equipment operation and maintenance system in the grain and oil processing industry employs advanced technologies and methods to enable real-time monitoring, fault diagnosis, predictive maintenance, and optimized equipment management. It monitors and evaluates equipment operating status through data acquisition, analysis, and processing; promptly identifies faults and abnormalities; and provides corresponding maintenance and improvement measures to ensure normal equipment operation and efficient production.
With the rapid development of the grain and oil processing industry, enterprises face many challenges in equipment management. Therefore, the role of equipment operation and maintenance systems in the grain and oil processing industry has become increasingly prominent. In future grain and oil industry development plans, intelligent equipment operation and maintenance systems can ensure rapid and efficient system operation through technological means, thereby improving production efficiency, reducing production costs, and enhancing safety and reliability. Intelligent operation and maintenance systems significantly advance the development and competitiveness of grain and oil processing enterprises. Starting from the existing problems in equipment operation and maintenance in the grain and oil processing industry, this article analyzes and discusses the current development status of fault diagnosis and prediction technologies for grain and oil processing equipment, and summarizes the characteristics and advantages of intelligent operation and maintenance systems, aiming to provide references for the future development of intelligent operation and maintenance systems in grain and oil processing enterprises.
1 Analysis of Existing Problems in Equipment Operation and Maintenance in the Grain and Oil Processing Industry
1.1 Low Level of Intelligence
Equipment operation and maintenance methods in the grain and oil processing industry have developed into a relatively stable workflow, but there is limited adoption of new technologies. The introduction of intelligent equipment and systems is relatively limited. From an economic cost perspective, the intelligentization of equipment operation and maintenance systems in the grain and oil processing industry requires significant investment, including equipment upgrades, software development, and training. Due to intense industry competition, enterprises may prioritize economic benefits and adopt a reserved attitude toward intelligent investment. Additionally, due to long-term reliance on traditional manual operation methods, operation and maintenance personnel have low acceptance of intelligent systems, and their awareness and understanding may be insufficient, with limited promotion and support for intelligent systems. At the same time, the grain and oil processing industry lacks standards and norms for the operation and maintenance of intelligent equipment, resulting in a lack of unified guidance and requirements for equipment suppliers when designing and developing intelligent systems. This also makes it somewhat difficult for enterprises to choose and apply intelligent systems.
1.2 Insufficient Data Collection and Analysis Capability
Equipment in the grain and oil processing industry is largely traditional, lacking modern automation and intelligent technologies, which makes automated data collection and real-time monitoring difficult. This leads to difficulties and inadequacies in data collection. In some older factories, equipment for collecting operation and maintenance data may be missing or incomplete. For example, the absence of sensors and monitoring instruments makes it impossible to accurately determine equipment operating status and performance data. Furthermore, in data collection, analysis, and management, the grain and oil processing industry may have limited capacity to analyze equipment operation and maintenance data, owing to a lack of professional data analysts and analytical tools. This prevents the integration of data with operational and maintenance work, thereby forming an effective data management and application mechanism. As a result, analysis of equipment operating status and performance is insufficiently deep or accurate, making it impossible to promptly identify issues and perform predictive maintenance, thereby preventing the full realization of the significance and value of data collection and analysis.
1.3 Non-standard Equipment Repair and Maintenance Management
In equipment repair and maintenance management in the grain and oil processing industry, factories may currently lack clear repair and maintenance plans, making it difficult to conduct timely equipment inspections and maintenance, which can easily lead to equipment failures and aggravated damage. Additionally, due to a lack of professional equipment repair and maintenance personnel, the quality of such work cannot be guaranteed. Personnel without professional training and certification may be unable to correctly diagnose equipment faults or take appropriate repair and maintenance measures. Furthermore, the lack of standardized guidance for equipment repair and maintenance, along with the absence of clear operating norms and procedures, leads to inconsistent quality in repair and maintenance work, thereby facilitating randomness and non-standard practices. Most importantly, the equipment repair and maintenance management system in the grain and oil processing industry is insufficiently robust, with issues including insufficient funding and resources and a lack of effective supervision and management mechanisms. The absence of supervision and evaluation of repair and maintenance work makes it difficult to promptly identify problems and address shortcomings.
1.4 Low Level of Informatization
Equipment in the grain and oil processing industry is mostly traditional, lacking modern automation and intelligent technology, making it difficult to achieve the informatization of equipment operation and maintenance systems. At the same time, the collection and management of equipment operation and maintenance data in the grain and oil processing industry may be incomplete, with a lack of standardized data collection methods and processes. In some factories, old and new equipment are used together, making it impossible to collect equipment operating status data in a timely, centralized, detailed, and complete manner, thus preventing automated data collection and real-time monitoring of equipment operation and maintenance. This situation limits the level of informatization of equipment operation and maintenance systems. Additionally, the grain and oil processing industry lacks emphasis and impetus on informatization, with potentially insufficient investment in the informatization of equipment operation and maintenance systems, thereby preventing the purchase and updating of information technology equipment and software. This, to some extent, limits the advancement of informatization, resulting in low levels of informatization in equipment operation and maintenance systems.
2 Overview of the Development of Fault Diagnosis and Prediction Technologies for Grain and Oil Processing Equipment
The development of fault diagnosis and prediction technologies for grain and oil processing equipment aims to improve equipment reliability and stability, reduce downtime due to faults, and enhance production efficiency and economic benefits. With the ongoing advancement of technology, fault prediction methods for grain and oil processing equipment are also evolving. Historically, fault prediction in grain and oil processing equipment relied primarily on traditional experience and statistical analysis. This method analyzes and mines historical equipment data to identify fault patterns and characteristics, and then builds corresponding fault prediction models. Although this method is simple and easy to implement, it relies on experience and statistical data and cannot accurately predict hidden faults and sudden faults. With the development of big data and artificial intelligence, data-driven fault prediction technologies have gradually emerged.
2.1 Traditional Fault Diagnosis and Prediction Methods
2.1.1 Vibration Analysis
Vibration analysis is a method for detecting faults in equipment by monitoring its vibration signals. By installing vibration sensors, equipment vibration signals can be collected in real time. Parameters such as vibration amplitude and waveform are used to identify equipment faults, including bearing wear, imbalance, and loosening. The advantage of vibration analysis is that it can detect equipment faults in advance, thereby preventing damage and production interruptions.
2.1.2 Infrared Thermography
Infrared thermography is a non-destructive testing method that detects equipment faults by measuring infrared radiation emitted from the equipment surface. By capturing infrared thermal images of the equipment surface, the temperature distribution can be observed. By analyzing the thermal images, abnormal conditions such as hot spots, overheating, and poor cooling can be identified, thereby recognizing equipment fault types, such as motor winding overheating and friction in transmission components. Infrared thermography can quickly and non-contact detect equipment faults, enabling early maintenance measures to prevent damage and production interruptions.
2.1.3 Other Methods
In addition to vibration analysis and infrared thermography, other common fault-diagnosis methods for grain and oil processing equipment include sound analysis, oil analysis, and current analysis. These methods can monitor and analyze equipment-specific signals or parameters to detect faults and implement appropriate maintenance and improvement measures, ensuring normal equipment operation and improving production efficiency.
2.2 Intelligent Fault Diagnosis and Prediction Technologies
2.2.1 Big Data-Driven Fault Prediction Technology
Big data prediction technology uses algorithms such as machine learning and deep learning to establish fault prediction models by collecting and analyzing equipment operating data. This method can more accurately predict equipment faults and monitor equipment status in real time, enabling early maintenance and repair to prevent downtime and damage.
2.2.2 IoT-Driven Fault Prediction Technology
With the advent of the information age, the development of the Internet of Things (IoT) technology provides new ideas and methods for fault prediction in grain and oil processing equipment. By connecting equipment to sensors, the internet, and cloud computing, real-time monitoring and analysis of equipment data can be achieved, enabling more accurate prediction of equipment faults. At the same time, IoT technology can enable remote monitoring and management of equipment, thereby improving operational efficiency and reliability.
3 Advantages of Predictive Maintenance-Based Intelligent Equipment Operation and Maintenance Systems
3.1 Improving Production Efficiency
Intelligent equipment operation and maintenance systems can significantly improve production efficiency. First, by using sensors and monitoring equipment to collect operating data in real time and performing analysis and prediction, intelligent operation and maintenance systems can promptly identify equipment faults and abnormalities, enabling early maintenance and repair to prevent equipment downtime and production interruptions, thereby improving production efficiency. Second, intelligent operation and maintenance systems can automate equipment repair and maintenance, reducing manual intervention and improving operational accuracy and efficiency. Automated operations can also reduce human errors and operational mistakes, further improving production efficiency. Additionally, intelligent operation and maintenance systems can conduct in-depth analysis of equipment operating data, identify potential problems and bottlenecks, and provide optimization recommendations. By optimizing and improving production processes, production efficiency and product quality can be enhanced. Furthermore, intelligent operation and maintenance systems can provide early warnings of equipment faults by analyzing equipment operating data and respond quickly. This means that measures can be taken before equipment faults cause significant impacts, thereby reducing downtime and production losses and improving production efficiency. Finally, intelligent operation and maintenance systems can achieve remote monitoring and management of equipment, allowing managers to understand equipment operating status and performance in real time, regardless of location. This enables timely identification and remediation of problems, thereby improving equipment utilization and production efficiency.
3.2 Reducing Production Costs
Intelligent equipment operation and maintenance systems can reduce production costs. First, intelligent operation and maintenance systems include fault-warning and preventive maintenance functions, which can prevent production downtime and repair costs associated with equipment faults, thereby reducing maintenance and equipment-replacement expenses. Second, intelligent operation and maintenance systems can automate equipment maintenance and repair operations, reducing the need for manual intervention, saving human resources, and lowering the costs of manual maintenance and repair. At the same time, intelligent operation and maintenance systems improve equipment utilization by optimizing equipment operation and production processes, reducing idle time and waste, and further lowering production costs. Finally, intelligent operation and maintenance systems can provide precise equipment operating data and maintenance recommendations, thereby extending equipment lifespan, delaying equipment replacement, and reducing equipment procurement and replacement costs.
3.3 Enhancing Safety and Reliability
Intelligent equipment operation and maintenance systems can improve safety and reliability. In the modern grain and oil processing industry, intelligent equipment operation and maintenance systems provide real-time monitoring and warning capabilities, thereby significantly reducing the likelihood of equipment faults and accidents. Using data analysis and machine learning algorithms, intelligent operation and maintenance systems diagnose and predict equipment faults, promptly identify potential fault risks, and implement appropriate repair or preventive measures, thereby significantly improving equipment reliability and stability. Additionally, intelligent equipment operation and maintenance systems can achieve remote monitoring and operation of equipment, enabling remote management and control of equipment, gradually realizing automation and intelligence in equipment operation and maintenance, improving the accuracy and efficiency of equipment operation and maintenance, reducing risks associated with manual operations, and enhancing equipment safety and reliability to a certain extent.
4 Conclusion and Outlook
With the ongoing development and application of technology, intelligent equipment operation and maintenance systems in grain and oil processing are continually improving. Future development trends may involve the comprehensive integration of traditional technologies, data-driven fault prediction, and IoT technologies, among others. Through the collaboration of multiple means, real-time monitoring, fault diagnosis, predictive maintenance, and optimized management of equipment in the grain and oil processing industry can be achieved. Such a predictive maintenance-based intelligent equipment operation and maintenance system will become increasingly mature and complete. Related research will make positive contributions to the intelligent transformation and economic benefits of the grain and oil processing industry and has broad application prospects in this sector.
