mud pump condition monitoring made in china

Valmet DNA Condition Monitoring is an online monitoring system. By utilizing wireless or wired sensors and highly developed applications, the system provides real-time vibration condition monitoring data available anywhere, anytime. Valmet DNA Condition Monitoring can be used as a standalone solution or as a built-in part of the Valmet DNA Automation System, enabling all the relevant process and condition monitoring data to be available in a single system.

Valmet Maintenance Pad is a portable data collector and analyzer for route-based vibration condition monitoring, predictive maintenance tasks, and advanced vibration measurements. The collector consists of a rugged tablet PC, analysis software, and wireless measurement units.

Valmet’s comprehensive services for condition and runnability monitoring include everything from audits to planning, analysis, and diagnosis with expert specialist support. Our in-depth know-how helps you ensure board and paper, tissue, pulp, as well as energy production applications, run smoothly. The availableservicescan be an integral turnkey function in your monitoring program or part of a targeted effort to resolve issues or improve performance. We can easily customize services to suit requirements according to location, application process area, or specific machinery.

Our Analysis and Diagnosis service can be as simple as remote expert support for your condition monitoring organization, or Valmet can carry out regular online or periodical offline monitoring for you. Specialist Services include technical support for specific problems, such as bearing or gear wear, or full system maintenance using online vibration monitoring. For internal teams, we also offer a range of training services to help your operators carry out monitoring tasks themselves.

Beyond products cover but not limited to: oil drilling & workover rig, water well drilling rig, pile drilling rig, horizontal directional drilling rig, mud pump and spares, solid control system & equipment, and other different drilling materials. Dedicated to strict quality control and thoughtful customer service, our experienced staff members are always available to discuss your requirements and ensure full customer satisfaction. We can customize the product supply as per your on-site conditions, and we can offer overseas after-sale services with our strong technical support. Thanks to our professional services, quality products, fast delivery and competitive prices, we have built up a very good reputation among our customers. Our products had covered countries like USA, Venezuela, Argentina, India, Singapore, Kazakhstan, Azerbaijan, Egypt and Ethiopia.

The function of the air bag capsule on the mud pump is to use the volume of the air bag itself to continuously store and compensate the liquid discharged to a part of the pump in the pipeline to reduce the passiveness due to the pumped liquid. When the instantaneous displacement of the mud pump increases above its average displacement, the instantaneous pressure in the discharge line rises, and the gas volume in the air bag is compressed.

Drilling consumables such as mud pump systems and their components can drastically increase your uptime while reducing costs and health/safety/environmental (HSE) risks. To support your drilling needs, Forum’s patented P-Quip® mud pump system offers a single-source solution that integrates high-quality fluid end components for maximum longevity and performance.

With more than 20 years of successful operation in severe environments, P-Quip offers a proven track record for the lowest cost of ownership in the industry. As part of our commitment to quality, our mud pump parts use patented Banded Bore™ technology that significantly reduces stress concentrations and leads to longer module life.

RVL-160, RVL-165 & RVL-165EX: Velocity RMS sensors, top-exit. Typically used for continuous overall vibration level monitoring in industrial control systems.

Researchers have shown that mud pulse telemetry technologies have gained exploration and drilling application advantages by providing cost-effective real-time data transmission in closed-loop drilling operations. Given the inherited mud pulse operation difficulties, there have been numerous communication channel efforts to improve data rate speed and transmission distance in LWD operations. As discussed in “MPT systems signal impairments”, mud pulse signal pulse transmissions are subjected to mud pump noise signals, signal attenuation and dispersion, downhole random (electrical) noises, signal echoes and reflections, drillstring rock formation and gas effects, that demand complex surface signal detection and extraction processes. A number of enhanced signal processing techniques and methods to signal coding and decoding, data compression, noise cancellation and channel equalization have led to improved MPT performance in tests and field applications. This section discusses signal-processing techniques to minimize or eliminate signal impairments on mud pulse telemetry system.

At early stages of mud pulse telemetry applications, matched filter demonstrated the ability to detect mud pulse signals in the presence of simulated or real noise. Matched filter method eliminated the mud noise effects by calculating the self-correlation coefficients of received signal mixed with noise (Marsh et al. 1988). Sharp cutoff low-pass filter was proposed to remove mud pump high frequencies and improve surface signal detection. However, matched filter method was appropriate only for limited single frequency signal modulated by frequency-shift keying (FSK) with low transmission efficiency and could not work for frequency band signals modulated by phase shift keying (PSK) (Shen et al. 2013a).

In processing noise-contaminated mud pulse signals, longer vanishing moments are used, but takes longer time for wavelet transform. The main wavelet transform method challenges include effective selection of wavelet base, scale parameters and vanishing moment; the key determinants of signal correlation coefficients used to evaluate similarities between original and processed signals. Chen et al. (2010) researched on wavelet transform and de-noising technique to obtain mud pulse signals waveform shaping and signal extraction based on the pulse-code information processing to restore pulse signal and improve SNR. Simulated discrete wavelet transform showed effective de-noise technique, downhole signal was recovered and decoded with low error rate. Namuq et al. (2013) studied mud pulse signal detection and characterization technique of non-stationary continuous pressure pulses generated by the mud siren based on the continuous Morlet wavelet transformation. In this method, generated non-stationary sinusoidal pressure pulses with varying amplitudes and frequencies used ASK and FSK modulation schemes. Simulated wavelet technique showed appropriate results for dynamic signal characteristics analysis.

As discussed in “MPT mud pump noises”, the often overlap of the mud pulses frequency spectra with the mud pump noise frequency components adds complexity to mud pulse signal detection and extraction. Real-time monitoring requirement and the non-stationary frequency characteristics made the utilization of traditional noise filtering techniques very difficult (Brandon et al. 1999). The MPT operations practical problem contains spurious frequency peaks or outliers that the standard filter design cannot effectively eliminate without the possibility of destroying some data. Therefore, to separate noise components from signal components, new filtering algorithms are compulsory.

Early development Brandon et al. (1999) proposed adaptive compensation method that use non-linear digital gain and signal averaging in the reference channel to eliminate the noise components in the primary channel. In this method, synthesized mud pulse signal and mud pump noise were generated and tested to examine the real-time digital adaptive compensation applicability. However, the method was not successfully applied due to complex noise signals where the power and the phases of the pump noises are not the same.

Jianhui et al. (2007) researched the use of two-step filtering algorithms to eliminate mud pulse signal direct current (DC) noise components and attenuate the high frequency noises. In the study, the low-pass finite impulse response (FIR) filter design was used as the DC estimator to get a zero mean signal from the received pressure waveforms while the band-pass filter was used to eliminate out-of-band mud pump frequency components. This method used center-of-gravity technique to obtain mud pulse positions of downhole signal modulated by pulse positioning modulation (PPM) scheme. Later Zhao et al. (2009) used the average filtering algorithm to decay DC noise components and a windowed limited impulse response (FIR) algorithm deployed to filter high frequency noise. Yuan and Gong (2011) studied the use of directional difference filter and band-pass filter methods to remove noise on the continuous mud pulse differential binary phase shift keying (DBPSK) modulated downhole signal. In this technique, the directional difference filter was used to eliminate mud pump and reflection noise signals in time domain while band-pass filter isolated out-of-band noise frequencies in frequency domain.

Other researchers implemented adaptive FIR digital filter using least mean square (LMS) evaluation criterion to realize the filter performances to eliminate random noise frequencies and reconstruct mud pulse signals. This technique was adopted to reduce mud pump noise and improve surface received telemetry signal detection and reliability. However, the quality of reconstructed signal depends on the signal distortion factor, which relates to the filter step-size factor. Reasonably, chosen filter step-size factor reduces the signal distortion quality. Li and Reckmann (2009) research used the reference signal fundamental frequencies and simulated mud pump harmonic frequencies passed through the LMS filter design to adaptively track pump noises. This method reduced the pump noise signals by subtracting the pump noise approximation from the received telemetry signal. Shen et al. (2013a) studied the impacts of filter step-size on signal-to-noise ratio (SNR) distortions. The study used the LMS control algorithm to adjust the adaptive filter weight coefficients on mud pulse signal modulated by differential phase shift keying (DPSK). In this technique, the same filter step-size factor numerical calculations showed that the distortion factor of reconstructed mud pressure QPSK signal is smaller than that of the mud pressure DPSK signal.

Study on electromagnetic LWD receiver’s ability to extract weak signals from large amounts of well site noise using the adaptive LMS iterative algorithm was done by (Liu 2016). Though the method is complex and not straightforward to implement, successive LMS adaptive iterations produced the LMS filter output that converges to an acceptable harmonic pump noise approximation. Researchers’ experimental and simulated results show that the modified LMS algorithm has faster convergence speed, smaller steady state and lower excess mean square error. Studies have shown that adaptive FIR LMS noise cancellation algorithm is a feasible effective technique to recover useful surface-decoded signal with reasonable information quantity and low error rate.

Different techniques which utilize two pressure sensors have been proposed to reduce or eliminate mud pump noises and recover downhole telemetry signals. During mud pressure signal generation, activated pulsar provides an uplink signal at the downhole location and the at least two sensor measurements are used to estimate the mud channel transfer function (Reckmann 2008). The telemetry signal and the first signal (pressure signal or flow rate signal) are used to activate the pulsar and provide an uplink signal at the downhole location; second signal received at the surface detectors is processed to estimate the telemetry signal; a third signal responsive to the uplink signal at a location near the downhole location is measured (Brackel 2016; Brooks 2015; Reckmann 2008, 2014). The filtering process uses the time delay between first and third signals to estimate the two signal cross-correlation (Reckmann 2014). In this method, the derived filter estimates the transfer function of the communication channel between the pressure sensor locations proximate to the mud pump noise source signals. The digital pump stroke is used to generate pump noise signal source at a sampling rate that is less than the selected receiver signal (Brackel 2016). This technique is complex as it is difficult to estimate accurately the phase difference required to give quantifiable time delay between the pump sensor and pressure sensor signals.

As mud pulse frequencies coincide with pump noise frequency in the MPT 1–20 Hz frequency operations, applications of narrow-band filter cannot effectively eliminate pump noises. Shao et al. (2017) proposed continuous mud pulse signal extraction method using dual sensor differential signal algorithm; the signal was modulated by the binary frequency-shift keying (BFSK). Based on opposite propagation direction between the downhole mud pulses and pump noises, analysis of signal convolution and Fourier transform theory signal processing methods can cancel pump noise signals using Eqs. 3 and 4. The extracted mud pulse telemetry signal in frequency domain is given by Eqs. 3 and 4 and its inverse Fourier transformation by Eq. 4. The method is feasible to solve the problem of signal extraction from pump noise,

These researches provide a novel mud pulse signal detection and extraction techniques submerged into mud pump noise, attenuation, reflections, and other noise signals as it moves through the drilling mud.

The ballasted track currently remains one of the few leading types of railway track structures due to the advantages in construction and maintenance [1,2]. However, the particulate nature of ballast particles often leads to performance degradation of ballasted trackbed. For example, the abrasion and breakage of ballast particles intensify with increasing axle load and train speed, thus causing the unfavorable densification, fouling, and clogging (i.e., reduced drainability) problems in ballasted tracked [3,4,5]; consequently, mud pumps, among other commonly observed track problems, can be prompted within such fouled ballasted trackbed [6,7]. Mud pumps could seriously degrade track stiffness and thus endanger operational safety of railway trains [8,9,10]. Traditional manual inspection and detection of mud pumps and other track problems are often labor-intensive, time-consuming, and subjective in nature; therefore, it becomes indispensable to develop automated, intelligent, and accurate means for the early-age diagnosis and detection of mud-pumping risks in ballasted trackbed so that remedial maintenance measures can be timely taken according to real-time health condition rather than the fixed schedules.

The root cause of mud-pumping fault has remained a widely-studied but challenging topic. Tadatoshi [11] proposed a suction-driven model and showed that the main cause of mud pumps is the intrusion of fine particles from the subgrade generated by the suction of ballast bed during the loading and unloading cycles. Raymond [12] found that the freeze-thaw cycles can cause fine-grained materials to pump out of the ballasted trackbed in winters according to a field performance investigation of the North American railway geotextiles. Duong et al. [13,14] believed that the interlayer materials between the subgrade and the ballasted trackbed were mainly generated by broken ballast particles, which then penetrated into the subgrade surface. The formerly Transportation Technology Center, Inc. (TTCI) established a field-testing zone to further study mud pumps [10,15,16,17]. Despite a considerable number of research studies have been carried out to explore the mechanisms of mud pumping fault, there still lacks radical countermeasures to prevent and control it in railway engineering practices.

The accurate early-age diagnosis and detection of mud pumps are the key step on which timely and effective prevention and control measures depend. The late-stage mud-pumping fault manifested on the surface of ballasted tracked is relatively easy to detect through routine labor-intensive methods; however, it is quite challenging to directly identify the early-age mud-pumping problem initiated inside the thick ballasted trackbed. The ground penetrating radar (GPR) technology has been widely applied in the non-destructive detection of structural faults in railway ballasted trackbed and subgrade [10,18,19,20]. Hugenschmidt [21] successfully applied GPR in the detection of railway subgrade problems for the first time in 1998. Since then, many countries including China have conducted related field and laboratory studies in this field [22,23]. Trong Vinh Duong et al. [13] carried out physical model tests and analyzed the influencing factors of the mud-pumping problem occurring in the interlayer between the ballast bed and underlying subgrade, including particle size distribution, moisture content, pore water pressure, hydraulic conductivity, etc. Kuo et al. [24] developed a characterized grid method and a scoring method to assess the mud-pumping distribution with an accuracy rate of 80%. Although the GPR technology has been reported to successfully detect visible or hidden mud-pumping problems in ballasted railway tracks [21], the accuracy and reliability of different GPR equipment and supporting post-processing software programs still vary considerably, not to mention the fact that they are highly costly and unaffordable for routine applications. In addition to GPR, other techniques have also been widely used for non-destructive detection of railway track foundation problems in recent years, such as the digital image correlation (DIC) [25,26,27], Interferometric Synthetic Aperture Radar (InSAR) [25], impact-echo method (IEM) [28], and synthetic aperture focusing technology (SAFT) [29,30]. However, these methods all require costly equipment and/or highly specialized skills that railway engineering practitioners usually lack. Therefore, to diagnose the in-service health condition and detect invisible problems of the ballasted trackbed accurately and reliably, it becomes imperative to explore automated, intelligent, and universally applicable methods in lieu of traditional ones.

The occurrence of mud pumps could cause uneven (or differential) rail track settlement and increasing track irregularities [31,32,33]. The existence of track irregularities could not only compromise the operational safety of heavy-haul trains but also degrade track substructures [34,35,36]. Li et al. [37,38] proposed a data-driven method for infrastructure deformation identification based on the characteristics of track geometry data, as well as a spatio-temporal identification model for identifying high-speed railway infrastructure deformation by using four years of track geometry data. Li et al. [39] analyzed the time and frequency characteristics of track geometry irregularity signals at the locations of mud pumps and used a multi-scale signal decomposition method to extract defect-sensitive features and then realize automatic detection of mud pumping problems. The nearly continuous and real-time track health monitoring of the entire rail networks could be possibly accomplished in a timely and cost-efficient manner by mounting robust sensors on in-service trains. For example, the problematic sections of railway track sub-structures were reportedly detected by using the vertical acceleration responses of a moving train [40]. Zeng et al. [41] proposed a data-driven approach for identifying mud pumps in the railway track substructure based on vibrational acceleration responses and Long Short-Term Memory (LSTM) artificial neural networks. The vibrational responses of ballastless slab tracks were also compared to detect the locations of mud pumps and study the feasibility of technical countermeasures to rectify and control the mud-pumping damage [42]. Therefore, analyzing the vibrational responses of the ballasted trackbed appears to be potentially helpful and promising for intelligent detection of mud-pumping problems in railway tracks.

Particle movement is a meso-scale manifestation of inter-particle contact behavior of ballast assemblies within the ballast bed; therefore, investigating the meso-scale movement characteristics of ballast particles may emerge as a promising, effective alternative to diagnose and identify the mud-pumping problem of ballasted tracked. The use of motion sensors (termed as “SmartRocks”) has been reported in the literature to directly capture real-time movement of ballast particles and then evaluate the field performance of ballasted trackbed under different in-service conditions [43,44,45,46,47]. The applications of such so-called SmartRock sensors in effective and accurate measurements of the vibrational responses of unbound aggregate particles including railroad ballasts were demonstrated in laboratory scaled model tests and triaxial tests [43,48,49,50]. Liu et al. [51] compared the ballast particle motion data measured by SmartRock sensors against those simulated by the discrete element method (DEM) model. Preliminary studies [52,53] suggested that SmartRock sensors could be used as a potential tool to quantify ballast behavior without using invasive measurement devices or disrupting railroad operations and to reflect the variations of dynamic behavior of ballasted trackbed under different substructure conditions. However, the widespread, reliable field applications of this new smart sensing technology for detecting invisible track defects such as mud pumps within ballasted trackbed remains to be extensively explored.

The purpose of this paper was to further study and substantiate the feasibility of SmartRock sensors in real-world field applications to diagnose and identify mud-pumping risks in ballasted trackbed. Therefore, a typical section of heavy-haul railway ballast bed with severe mud pumping problems was chosen for investigation, where the SmartRock sensors were employed and instrumented accordingly to monitor particle-scale acceleration responses prior to, during, and after major maintenance operations including ballast-cleaning and tamping. The three-dimensional (3D) acceleration responses and associated marginal spectra of ballast particles recorded by SmartRock sensors in different positions were comparatively analyzed for the initial degraded and subsequent rectified scenarios of the ballast bed. The findings are expected to contribute to the optimization of maintenance operation parameters and smart track health monitoring.

As shown in Figure 1, slurry pump remote online monitoring system of the present invention, tested slurry pump 1 state signal imports collecting device 3 into by signal cable; The process of the data collector 4 in collecting device, switchboard 5, photovoltaic converter 6, the optical signal of final output is sent to central control chamber 8 by optical fiber, through photovoltaic converter or the conversion of photoelectricity switchboard, by netting twine access to LAN, monitoring center 9 obtains slurry pump 1 runnability by access Local Area Network or internet, carries out technical support.

Separate unit slurry pump 1 adopts 12 to vibrate measuring point, 1 rotating speed measuring point, the vibration gathered by sensor and tach signal, by modbus-tcp protocol module access data collector 4, from the data-signal that data collector 4 exports, after switchboard 5, access photovoltaic converter 6 again, convert to optical signal laggard enter optical fiber ring network.

Every platform slurry pump 1 configures 3 AIC9000 data collectors 4, and each data collector is 12 passages, wherein four-way vibration, two passage rotating speeds, and rest channels is reserved cached variable, supports extended channel at any time.

The vibration of described slurry pump and tach signal comprise the vibration velocity, vibratory impulse and the speed-frequency that produce at work, monitoring system is by each delta data when sensor, the work of vibration pickup Real-time Collection slurry pump keypoint part, and these data are passed to the monitor server in central control chamber 8, the relevant monitor signal numerical value of display in real time on display device in central control chamber, and store in storage.

Central control chamber 8 monitor server is default preserved a record automatically at interval of 1 minute, and every bar monitoring record comprises vibration original waveform, the frequency spectrum of slurry pump vibration measuring point.

Monitoring Data puocessing module: adjust acquisition parameter on demand according to failure mode, acquisition parameter comprises port number, analysis frequency, sampling number, low pass setting, the setting of low pass flex point, high pass, anti-mixed setting, envelope setting, triggering mode;

The present invention utilizes informationization technology, to slurry pump units" installation on-line monitoring system, by the primary signal of operation field sensor and real time data remote transmission to server on base, realize the adjustment of operation field sensor, data capture, monitoring by server on base, base monitor terminal and realize the accurate judgement to mud pump equipment state by auxiliary diagnosis function and expert diagnosis, through multiple network platform distributing data, realize data sharing, technician at different levels is facilitated better to grasp exploration dynamically on-the-spot, guide field plant maintenance and maintenance.

In slurry pump remote online monitoring system implementation process, software and hardware effect is good, and entire system is environmentally withstanding the test of sleet, foggy weather, and anti-lightning, superiority that is anti-interference, failure rate is low are well represented.The aspect stable performances such as data capture at the scene, long-range transmission, base reception, remote terminal, telecontrol, the every complete data of slurry pump remote online monitoring system is accurate, and various Rational Parameters is effective.

In the present embodiment, separate unit slurry pump adopts 12 to vibrate measuring point, 1 rotating speed measuring point.The vibration that sensor gathers and tach signal, by modbus-tcp protocol module access AIC9600 data collector, from the data-signal that data collector exports, access photovoltaic converter 6 after TP-LINK conversion.Three slurry pump configure 9 AIC9000 data collectors, and each data collector is 12 passages, wherein four-way vibration, two passage rotating speeds, and rest channels is reserved cached variable, supports extended channel at any time.

Adopt AIC9600 system Optical Fiber Transmission scheme: as shown in Figure 1, tested slurry pump 1 state signal imports collecting device 3 into by signal cable 2; The process of the data collector 4 in collecting device, switchboard 5, photovoltaic converter 6, final output optical signal; Be sent to central control chamber 8 by optical fiber, again utilize photovoltaic converter or the conversion of photoelectricity switchboard; Use netting twine access to LAN, monitoring center 9 can be understood machine operation by access Local Area Network or internet or be carried out technical support.

Monitoring system can automatic timing record Monitoring Data, and uploads monitor server and form SQL monitor database.A record was preserved automatically at interval of 1 minute in system default, can revise deposit interval when needing.Every bar monitoring record comprises vibration original waveform, the frequency spectrum of 36,3 slurry pump vibration measuring point, analyzes frequency range 5000Hz, spectral line number 1600.

(1) the online long-range continuous sampling analysis software module of slurry pump comprises: 1) waveform parameter: peak value, mean value, effective value, peak index, waveform index, pulse index, margin index, kurtosis index; 2) frequency content: various fault characteristic frequency frequency multiplication composition presses the large minispread of peak value, all primary oscillation source of fast finding (position of abnormal vibrations and reason).3) specific aim image data: adjust acquisition parameter on demand as " port number according to failure mode, analysis frequency, sampling number, low pass is arranged, low pass flex point, high pass is arranged, anti-mixed setting, envelope is arranged, triggering mode etc. " 4) various analytic function: time-domain analysis, frequecny domain analysis, correlation analysis, probability analysis, wavelet analysis, trends analysis, Phase analysis, time is three-dimensional, rotating speed three-dimensional (start and stop analysis), axle center locus, Bode diagram, nyquist diagram.5) sampled data mode: online, off-line two kinds of analysis modes can be carried out.Detecting engineer uses portable computer to pass through RJ45 network interface connection monitoring hardware, analysis software just can be utilized to carry out on-line analysis, utilize expert system to carry out labor.6) vibration data two kinds of analytic functions: the batch processing analysis of vibration data, to analyze separately.Vibrate the work that batch facility is generally used for multiple spot single, multiple spot repeatedly continuous shaking data carry out searching alarm numerical value, after alarm numerical value to be found, carry out independent labor again.Independent analytic function is used for carrying out comparatively labor to single-point single continuous shaking data, can show the time domain under stable rotation operating mode, frequency curve, the numerical value such as display frequency, amplitude, 1X frequency multiplication, 2X frequency multiplication, 3X frequency multiplication, 4X frequency multiplication.Cursor follows frequency domain, time-domain curve display single-point numerical value, the alarm of display amplitude, alarm oscillating signal place sample point particular location etc.

Slurry pump produces hydraulic flow, rotating speed, temperature and vibration at work, monitoring system is by each delta data when various sensor, the work of vibration pickup Real-time Collection slurry pump keypoint part, and these data are passed to storage, the relevant monitor signal numerical value of display in real time in LCD Display, and store in storage.

Monitoring system, after the various signal data of Real-time Collection, vibration data, is compared with the warning gate data preset.When image data is higher than Alert data, monitoring system automatically carry out On Screen Display report to the police and light, audible alarm.

The oscillating signal collected can temporarily be stored in storage by the built-in hardware device such as storage, data/address bus, interface equipment of monitoring system, detecting engineer also can be connected on the interface equipment of system by portable computer, is read on portable computer by the sampled signal file of storage.Also the device analysis server in base can be sent data in real time by 3G network, remote monitoring on-the-spot all kinds of sampled data of equipment can be analyzed.

Analysis software provides analogue simulation fault and expert system diagnosis suggestion function for single-point single continuous sampling data.When detecting engineer and carrying out labor to certain single-point single continuous sampling data, analysis software can provide slurry pump rotation situation and the point position of three-dimensional artificial, from rotary work start, with representing 30 minutes real-time times 30 seconds, continuous display was from 30-120 minute field evidence, the numerical value change of display different measuring points and column temperature rise effect, the time domain of display fault point, frequency-domain waveform, display vibrating alert numerical value, display vibration amplitude alarm gate numerical value, showing temperature accuse numerical value, display alarm temperature gate section.The expert diagnosis storehouse that software carries simultaneously provides expert diagnosis conclusion, display vibration experts database diagnosis, the position at the hidden danger alarm position of structural drawing display simultaneously and device situation.

Analysis software carries expert diagnosis function, to failure condition, the vibrating numerical preset failure deagnostic structure of the keypoint parts such as gear, bearing, motor, valve, when sampled data and preset data are coincide, analysis software automatically provides the prompting of expert diagnosis conclusion and detects engineer.Analysis data and analysis result in conjunction with the modal analysis detecting engineer, maintenance engineering teacher, can also combine by analysis software simultaneously, and write expert diagnostics information storehouse, with gradual perfection.

The present invention utilizes informationization technology to carry out lifting means managerial skills, to slurry pump units" installation on-line monitoring system, automatic alarm is carried out to unit exception state in the basis of the complete status information of equipment of Real-time Collection, and utilize auxiliary diagnosis function and the accurate judgement of expert diagnosis realization to equipment state, make equipment management personnel grasp the state of Key generating unit real-time and accurately.