Scheme Analysis | Embarking on a 'New' Journey: Building a New High Ground for Predictive Maintenance in Intelligent Automotive Manufacturing

With the development of the automobile industry, China's auto parts industry market has also become popular. The intelligent development of automobile manufacturing is also developing rapidly. With the deepening of information technology and intelligent revolution, especially the widespread application of Internet technology in manufacturing, it has a positive role in promoting manufacturing innovation, organizational structure, and production models, and it also puts forward new requirements for it.

Precise perception is the basis of industry intelligence. To solve the problem of intelligent perception in the complex production environment of this industry, the following problems must be solved first:

01. Workshop equipment is not connected to the network, and the on-site situation cannot be understood in time.

1). How is the equipment running?

2). What is the equipment utilization rate?

3). Does the operator operate improperly?

4). During the equipment processing, will it affect the product quality?

5). How much energy does the equipment consume?

02. Data islands, numerous systems, and insufficient integration with business

1). Insufficient business traction, insufficient system application depth, and difficulty in supporting data-driven business

2). Unable to automatically obtain equipment information, low utilization of existing data, unable to provide decision-making for production business

3). Lack of effective communication between departments, leading to unscientific operation of business modules, repeated modification and improvement of data and other information, resulting in serious cost waste

03. The original equipment management method cannot record and trace the process in real time.

1). Equipment inspection is carried out through paper records, which is inconvenient for repeated review and analysis.

2). The equipment inspection, repair, and maintenance process is complex, and the employee's operation cannot be effectively supervised and traced.

3). The equipment fault handling and maintenance information records are simple and unclear, and cannot conduct a comprehensive health assessment of the equipment. The equipment fault handling is mainly based on post-maintenance.

Traditional maintenance methods are mainly based on spot inspections, planned overhauls, and post-maintenance. Through the informatization and intelligence of enterprise equipment management, promoting enterprise digital transformation, integrating equipment intelligent diagnosis technology, and combining with planned overhauls, preventive maintenance, and spot inspection and maintenance, the precise maintenance and overhaul capabilities of equipment are improved.

Common fault types:

01 Mechanical failure

Mechanical failures usually manifest as abnormal equipment operation, excessive vibration, abnormal sounds, etc. Mechanical failures are usually caused by long-term equipment operation, component wear, insufficient lubrication, etc. Common mechanical failures include bearing damage, gearbox failure, and shaft seal leakage.

02 Electrical fault

Electrical faults usually manifest as abnormal operation of electrical equipment, motor overload, control failure, etc. Electrical faults are usually caused by aging wires, damaged electrical components, control logic errors, etc. Common electrical faults include motor winding short circuits, circuit breaker tripping, and sensor failure.

03 Control fault

Control faults usually manifest as the control system not responding or responding abnormally, data transmission errors, etc. Control faults are usually caused by control system hardware or software failures, signal interference, incorrect parameter settings, etc. Common control faults include PLC control module damage, data acquisition errors, and regulating valve failure.

04 Faults caused by environmental factors

Faults caused by environmental factors usually manifest as decreased equipment performance and component damage. Environmental factors such as temperature, humidity, and dust may have an adverse effect on cement equipment, leading to decreased equipment performance or component damage. For example, high temperature may cause the lubricating oil to deteriorate, high humidity may cause equipment rust, and dust may cause control components to fail.

05 Common faults in automobile parts manufacturing

1. In the case of lack of oil, the main drive shaft and bearings are damaged;
2. In the case of oil blockage, the bronze bushing is damaged, and copper powder falls off;
3. Lack of oil at the serrated part causes jamming;
4. The gap between the bronze bushing and the shaft increases, the comprehensive gap increases, and the processing accuracy is affected
5. Crankshaft fracture and cracks.

06 Faults caused by overheating

During the inspection process of the conveyor line, it was found that the rolling element or cage changed color, indicating that the bearing was overheated. When the instantaneous temperature of the bearing reaches a certain value, in addition to the sharp decline in lubrication performance, the bearing operation may enter a dry grinding state, thereby aggravating the bearing failure; it will also lead to a decrease in the hardness of the rolling element, leading to a greater acute failure.

Imagine if the automobile manufacturing equipment could issue early warnings and provide solutions before a malfunction occurs. What would that experience be like? Zhongyun Technology's predictive maintenance solution was created for this!

Through real-time data analysis, machine learning algorithms, and Internet of Things technology, we can accurately predict potential equipment failures, allowing for proactive maintenance and repair, significantly reducing equipment downtime and maintenance costs.

Core Technology

Real-time data analysis:

By installing intelligent sensors on the equipment to collect various equipment status data, such as vibration, temperature, telecommunications, and oil, using data acquisition, transmission, and analysis technologies, 7x24-hour real-time monitoring is performed to quickly identify and perceive the operation or changes of the equipment. Through big data analysis technology, potential problems are discovered.

Machine learning algorithm:

It has the most comprehensive algorithm model in the industry. Nearly 100 diagnostic algorithm models cover more than 90% of industrial equipment failure models, including failure frequency, shaft center trajectory, waveform characteristic values, pulse speed, power spectrum, etc. A priori knowledge + small sample algorithm achieves an intelligent diagnosis accuracy rate of 95% for equipment failures, with zero missed reports.

Internet of Things technology:

Supports rapid access to device data from wired, Wi-Fi, 2G/4G, 5G, NB-IoT, LoRa, RJ45, RS485, Zigbee, and other transmission networks, seamlessly connecting devices to cloud platforms for remote monitoring and real-time control.

Economic Benefits

Reduced Consumption and Increased Efficiency:

By predicting equipment failures in advance, it avoids equipment failures during critical production stages, reducing production losses caused by equipment malfunctions. In addition, predictive maintenance reduces equipment downtime, improves equipment utilization, lowers maintenance difficulty and costs, and reduces unnecessary maintenance expenses. Simultaneously, optimizing production plans ensures equipment operates at peak performance, further enhancing production efficiency.

Safe Production:

Reduces production accidents, completely avoids production accidents caused by equipment, achieving zero missed reports to protect workers' lives and occupational health, reduces the number of inspections personnel entering hazardous areas, reduces inspection workload by 90%, reduces huge losses caused by work stoppages and rectification, and performs planned equipment maintenance with 95% accuracy.

With the continuous development of the automotive manufacturing industry, predictive maintenance of equipment will become an industry standard. Zhongyun Technology will continue to cultivate this field, continuously innovating and optimizing solutions to contribute to the sustainable development of the automotive manufacturing industry!