Abstract: To effectively monitor the operation of numerous asynchronous motors and their drive systems in industrial and mining enterprises, this paper proposes a distributed online monitoring and fault diagnosis system for asynchronous motors based on CAN bus and using the Texas Instruments TMS320LF2407 digital signal processor as its core. From an application perspective, this distributed monitoring system, utilizing detection and control technology, network technology, fieldbus technology, and configuration software technology, replaces the original single decentralized control mode. This system features networking, intelligence, and integration, achieving integrated control and management of motor operating status parameter detection, fault diagnosis, and fault handling. Keywords: CAN bus; TMS320LF2407 digital signal processor; online monitoring; fault diagnosis [b] [align=center] Design and Implementation of Distributing Asynchronous Motors State Monitoring and Fault Diagnosis system on Line Xie-yunmin[/align][/b] Abstract: In order to monitor the running status of legion asynchronous motors and the equipment efficiency in project spot, a sort of distributing asynchronous motors state monitoring and fault diagnosis system on line driven on CAN field-bus and Texas Instruments of USATMS320LF2407 DSP. From the point of view of appliance, the distributing monitoring system which included the technology of measure and control, network, CAN filed-bus, configuration software etc is replacing the single monitoring system of inhere. The system has characteristic of network, intelligent, integration. The control and manage incorporate of moving state parameter measure, fault diagnosis system, fault disposed had realized. Key Words： CAN filed-bus;TMS320LF2407DSP;State Monitoring on Line;Fault Diagnosis 1 Design Scheme Proposal In industrial and mining enterprises, the most important loads are various large and medium-sized asynchronous motors. Failures in these motors will severely impact production and cause huge economic losses. Furthermore, using existing relay control systems makes it difficult for staff to effectively monitor the operation of numerous asynchronous motors and their drive systems on the production line. From an application perspective, a monitoring system based on intelligent instrument technology and fieldbus technology, integrating networked, intelligent, and integrated control, has become essential to replace single, distributed control. Therefore, for the technical transformation of a cement plant, this paper proposes a distributed asynchronous motor online monitoring and fault diagnosis system based on CAN bus and using the Texas Instruments TMS320LF2407 digital signal processor as its core. This system achieves integrated safe operation, equipment protection, monitoring, and management of numerous motors and their drive systems in the enterprise. 2 System Composition 2.1 Distributed System Composition Faults in the stator core, stator winding insulation, rotor winding, rotor body, slip rings, and brushes are common faults in large and medium-sized asynchronous motors. Currently, commonly used online monitoring methods include stator current spectrum method, vibration signal spectrum method, and partial discharge method. Since this paper focuses on a single system, and these methods have high accuracy in diagnosing certain motor faults, the online monitoring and fault diagnosis system for large and medium-sized asynchronous motors discussed in this paper adopts the mature stator current spectrum method, which can comprehensively reflect the motor's operating status. The stator current spectrum method essentially uses the motor's stator winding as a detection coil in the monitoring device to participate in fault detection. When a certain fault occurs in the motor, the stator current shows a corresponding frequency domain change. Based on the different fault characteristic frequencies of the asynchronous motor stator current when different faults occur, we judge and handle the fault by detecting and comparing the fault frequencies. It is worth noting that for complex fault systems, the existence of faults has different fuzzy characteristics. An expert fuzzy subsystem can be used to collect as many fault signals as possible for comprehensive evaluation to further improve the accuracy of fault diagnosis. The system structure diagram is shown in Figure 1. The hardware of this system mainly consists of an industrial control computer, a field CAN bus interface module, a digital signal processor, and a signal acquisition module. [align=center]Figure 1. Structure diagram of a distributed asynchronous motor online monitoring and fault diagnosis system based on CAN bus and DSP[/align] 2.1.1 Host Computer The host computer uses an Advantech industrial control computer with Windows NT 4.0 as the operating system. The software is written using KingView 6.5 configuration software. The host computer has monitoring, real-time control, data logging, report printing, and database processing functions. KingView is a general-purpose industrial automation monitoring configuration software based on the Windows 98/2000/NT Chinese platform. It is a true 32-bit integrated software system. It has a full Chinese browser interface, is easy and flexible to use; real-time multi-tasking, multi-threading, fast sampling speed, and high reliability; it widely supports data acquisition devices such as PLCs, intelligent instruments, intelligent modules, boards, and frequency converters at home and abroad, and also supports OPC, ActiveX, ODBC, TCP/IP, DDE, fieldbus, modem, servo drive, etc.; data and system functions are fully open, with many powerful controls embedded, and many control algorithms (such as PID) built in. It also has the basic characteristics of excellent software continuity, expandability, encapsulation, and versatility. The intuitive configuration graphics not only allow managers to monitor the dynamic process of on-site production, but also visualize the operating status of equipment. Permissions are set for its control functions, such as switch operations and parameter settings. To further improve reliability, two host computers are configured, serving as backups for each other. 2.1.2 CAN Bus Monitoring System The CAN bus control system adopts a three-layer architecture. The industrial control computer and its application software constitute the monitoring and scheduling layer, the bus is the middle layer, and the data acquisition unit is the lower layer. The host computer consists of an industrial control computer and a network card. The network card is used to connect the industrial control computer to the CAN bus, i.e., to perform protocol conversion between RS-232 and CAN bus. The system structure diagram is shown in Figure 2. [align=center] Figure 2 CAN Bus Communication System Structure Diagram[/align] The industrial control computer for monitoring and scheduling connects to the CAN bus through the network card as a data buffer. The SJA1000 and PCA82C250 form a standard CAN bus, realizing data exchange with the digital signal processor. The CAN bus signal transmission uses differential level mode, which can effectively suppress common-mode interference. The communication controller uses the PHILIPS SJA1000, and the bus transceiver uses the PCA82C250, providing differential transmission and reception of the bus. Data uses a short frame structure with 8 bits of effective word length per frame, resulting in short transmission time and low probability of interference. The CAN bus transmission rate is 1Mb/s to 5Mb/s, and the transmission distance is 40m to 10km, meeting the communication requirements of the enterprise's site. 2.1.3 DSP System Hardware Circuit To ensure the designed system not only meets the user's requirements for excellent performance, easy maintenance, and reasonable price, but also possesses high reliability to adapt to the complex and harsh production environment, we selected the Texas Instruments TMS320LF2407DSP, known for its high cost-performance ratio, high reliability, and low power consumption, as the core of the online monitoring and fault diagnosis system for asynchronous motors in cement production lines. The DSP performs online signal acquisition, measurement and control, and fault handling for the asynchronous motor; after the DSP determines a motor fault, the LCD displays the fault location and issues an audible and visual alarm signal; sensing elements are used to detect the motor's current and voltage signals. The hardware circuit of the DSP system is shown in Figure 3. [align=center] Figure 3 Hardware circuit of the DSP system[/align] The TMS320LF2407 controller is a superset controller in the TMS320LF240X fixed-point digital signal processor, a low-cost, low-power, high-performance processor from Texas Instruments, and is very useful for the digital control of motors. It features an improved Harvard architecture, compatible with the TMS320 instruction code. Utilizing high-performance static CMOS technology, the supply voltage is reduced to 3.3V, decreasing the controller's power consumption; the 30MIPS execution speed shortens the instruction cycle to 30MHz, thereby improving the controller's real-time control capability. It has up to 32K words of FLASH program memory, 1.5K words of data/program RAM, 544 words of dual-port RAM (DARAM), and 2K words of single-port RAM (SARAM). It has two event management modules, EVA and EVB, each including: two 16-bit general-purpose timers, eight 16-bit pulse width modulation (PWM) channels, three capture units, and a 16-channel A/D converter. The LF2404 features 192K words of external memory (expandable to 64K words of program memory, 64K words of data memory, and 64K words of I/O addressing space). This fully meets the demands of various applications with stringent performance and power consumption requirements. It also ensures sufficient data for retrieval and analysis before and after a fault, satisfying the system's real-time data processing and storage needs. It includes a 10-bit A/D converter with a minimum conversion time of 500ns, and can be triggered by an event manager with two 8-channel input A/D converters and one 16-channel input A/D converter. Furthermore, it features a Controller Area Network (CAN) 2.0B module; a Serial Communication Interface (SCI); a phase-locked loop-based clock generator; 40 individually programmable or multiplexed general-purpose input/output pins (GPIO); and five external interrupts (two maskable interrupts for motor drive protection and reset). The DSP system uses current and voltage transformers to collect real-time current and voltage signals from the asynchronous motor during operation. After signal conditioning modules such as amplification and low-pass filtering, the signals are converted into digital signals by the DSP's A/D converter. The DSP then performs a Fast Fourier Transform (FFT) to transform the signals to the frequency domain. The DSP determines whether a motor fault has occurred based on whether the frequency domain contains characteristic frequencies corresponding to asynchronous motor faults. If a fault occurs, an audible and visual alarm is issued, and the fault location is displayed on the LCD. In the DSP's peripheral interface circuitry, the power supply chip is the TI TPS73HD318, whose DC outputs of 1.6V and 3.3V supply the DSP core and on-chip peripherals, respectively. The reset and voltage monitoring chip is the MAX706 integrated microprocessor monitoring and reset circuit from Maxim Integrated. The stator current and voltage signals of the asynchronous motor extracted by the current and voltage transformers are fed into the DSP's A/D converter after anti-aliasing filtering and sample-and-hold. The four analog inputs are configured as either four single-input channels or two differential input channels. It achieves phase-difference-free sampling across four analog channels. Sampling points can be programmatically set to meet system requirements. In this system, current and voltage are sampled 30 times per cycle, with a sampling frequency of 1500Hz. 2.2 System Working Principle: The signal acquisition module acquires the stator current and voltage signals of the monitored asynchronous motor. After a series of signal preprocessing and transformations, these signals are converted into digital signals acceptable to the DSP and sent to the DSP. The DSP performs a Fast Fourier Transform on the digital signals from the signal acquisition module, transforming the time-domain signal into a frequency-domain signal. Based on whether the frequency domain contains the characteristic frequencies of various motor faults, it determines whether a motor fault has occurred and the severity of the fault. The fault handling module, based on the output of the DSP diagnostic module, promptly issues audible and visual alarms to notify personnel to take appropriate action. If necessary, a portable computer can be connected to the system via its USB universal serial interface for further analysis and judgment of the motor fault location and severity on-site. 3. Software Design The system software was developed using TI's Integrated Development Environment (CCS) 2.0. The main program was programmed in C language, while other functional modules, such as the signal processing module and the fault characteristic frequency comparison and judgment module, were programmed in assembly language. The system program also utilizes TI's Bootloader technology, allowing the main program to be downloaded from the external FLASH ROM to the on-chip fast RAM for full-speed execution after a hardware reset, thus achieving system bootstrapping functionality. The entire program adopts a modular structure, mainly divided into five modules: system initialization, signal acquisition, signal processing, fault frequency comparison and judgment, and fault handling. Program execution is divided into four stages. The system program flowchart is shown in Figure 4. After system initialization, the signal processing and acquisition module first sends an interrupt to the main program every 1024 sampled values. Then, the signal processing module performs a Fast Fourier Transform (FFT) on the sampled data, decimating 1024 samples over time, to obtain the frequency domain information of the sampled data. The third stage involves the fault frequency comparison and identification module comparing and judging this frequency domain information with the internally stored fault characteristic frequency table. Finally, it determines whether a motor fault has occurred. If a fault has occurred, the fault handling module is invoked for processing. Otherwise, the system begins the next cycle of program execution. [align=center] Figure 4 Main Program Flowchart[/align] 4 Conclusion This paper introduces a distributed asynchronous motor online monitoring and fault diagnosis system based on CAN bus and DSP. This system overcomes the shortcomings of existing similar fault diagnosis systems, such as poor real-time performance and poor diagnostic performance, achieving certain practical effects and possessing promotional value. This system integrates microcomputer monitoring, detection and control technology, fieldbus technology, and configuration software technology to achieve integrated control and management of motor operating status parameter detection, fault diagnosis, and handling. It provides accurate data for online monitoring and control of the production process and a reliable guarantee for rapid fault diagnosis and handling. The author's innovation lies in designing a distributed asynchronous motor online monitoring and fault diagnosis system based on CAN bus and DSP. The system employs a modular structure in its hardware and software design, featuring strong real-time performance, good diagnostic capabilities, and high accuracy. References [1] Yang Fei, Zheng Guilin. Design of monitoring system based on CAN bus [J]. Microcomputer Information, 2005, 7:34-36 [2] Liu Heping. Structure, principle and application of TMS320LF240XDSP [M]. Beijing: Beijing University of Aeronautics and Astronautics Press, 2002 [3] Shi Jinhong, Qi Wei et al. Development of intelligent measurement and control protection system based on fieldbus [J]. Industrial Control Computer, 2006, 7:64-65 [4] Yu Qiongfang, Dong Aihua et al. Fuzzy expert system for fault diagnosis of large and medium-sized asynchronous motors based on DSP [J]. Coal Mine Electromechanical, 2003, 6:10-12 [5] He Yi. Design of online monitoring fault diagnosis system for large and medium-sized asynchronous motors based on DSP [J]. Mining Machinery, 2004, 12:67-68