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    Predictive Maintenance, also known as Predictive Maintenance (PDM), is the process of collecting data and monitoring the condition of equipment to predict potential failures before they occur.And before the occurrence of failure damage, put forward preventive measures to replace the relevant parts.Predictive maintenance has been recognized and applied on a global scale, especially in the field of industrial manufacturing.Condition monitoring, fault diagnosis, state prediction and state decision are integrated into one, condition monitoring and fault diagnosis are the foundation, state prediction is the key, maintenance decision to obtain the final maintenance state requirements, predictive maintenance is the application and practice of artificial intelligence in the field of industrial manufacturing.Compared with repair maintenance and preventive maintenance, predictive maintenance has the characteristics of low cost, low impact on production line, high replication and wide application scenarios.And intelligent sensor is the essential key equipment in predictive maintenance technology.
    Most of the applications of smart sensors in predictive maintenance technology are state-based detection (CBM) to measure the current health status of equipment.Predictive maintenance (PDM) involves a combination of techniques such as CBM, machine learning, and analytics to predict impending machine or asset failures.When monitoring the health of a machine, it is critical to select the most appropriate sensors to ensure that faults can be detected, diagnosed, and even predicted.A number of sensors are currently used to detect and detect faults in rotating machines and their loads, with the ultimate goal of avoiding unexpected downtime.Because PDM technology is used in a variety of rotating machines (motors, gears, pumps, and turbines) and non-rotating machines (valves, circuit breakers, and cables), common sensors can only be monitored for a single application scenario, resulting in a variety of sensors and complexity.The intelligent predictive sensor can monitor the state of the equipment in multiple scenes.
    The application of predictive maintenance smart sensors in Industry 4.0 is outpacing other industrial applications as more accessible and cost-effective solutions and technologies.This method is based on the combination of state monitoring and fault mode dynamic prediction model.Although a more complex overall system is indeed required, it has the advantage of optimizing machine life and improving factory productivity.Predictive Maintenance Smart Sensor allows corrective maintenance to be scheduled at the right time while maximizing the service life of equipment by preventing equipment failure.Knowing when and what needs to be done to maintain the machine allows you to best plan maintenance when the right people and parts are in place.
     Predictive maintenance intelligent sensor can be divided into two ways: direct sensing and indirect sensing. Through predictive maintenance intelligent sensor, the equipment status can be understood, and the correlation between remote sensing parameters and mechanical state can be understood.Direct sensing technology measures the actual number of others that directly indicate machine condition. As defects usually occur inside the machine, direct detection is usually achieved by dismantling or disrupting normal operations.Indirect sensing technology can indirectly measure various auxiliary parameters of the mechanical condition (such as force, vibration and noise) as the vibration (friction, or heat) due to the deficiency of the house changes.Compared with direct sensing, indirect sensing method is low cost and can continuously detect all changes (such as tool breakage, tool wear, etc.) without interrupting the normal operation of the machine.As the sensors become more and more intelligent, generally speaking, the more sensors are placed on the facilities, the more complex the information carried by the sensors, the more can reflect the state of the machine.However, in practice, the number of sensors is very limited and is limited by cost, installation and other factors.Therefore, the placement of the sensor needs to be considered in order to obtain as much information as possible about the machine.
     Based on the existing wired predictive maintenance intelligent sensor, a wireless predictive maintenance intelligent sensor for data transmission is developed according to the requirements of customers and the types of application scenarios.The connection between the sensor and the data collector is not always possible via cable, and radio technology is generally cheaper, more flexible and more durable.The new wireless predictive maintenance smart sensor is suitable for applications that do not use data collectors or data collectors that only need to transfer small amounts of data to the Internet due to strong data compression through edge computing.They can establish a direct Internet connection from the sensor to the cloud and transfer measurements to the cloud without the need for a separate gateway.