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Improving condition monitoring and diagnosis and realizing overall system optimization are some of the core challenges faced by people when using mechanical facilities and technical systems. This topic is becoming more and more important not only in industry, but also in any place where mechanical systems are used. In the past, the machine was maintained according to the plan, and delayed maintenance may face the risk of production shutdown. Nowadays, people predict the remaining service life of machines by processing their data. Especially for key parameters such as temperature, noise and vibration, the recorded data can be used to determine the best operation state and even the required maintenance times. This can avoid unnecessary wear and tear, and can find potential problems and causes as soon as possible. Through this condition monitoring, the availability and effectiveness of facilities can excavate a considerable optimization space, so as to obtain a decisive advantage. For example, the implementation of such monitoring has proved that abb1 has reduced downtime by 70% in one year, extended the service life of motors by 30%, and reduced the energy consumption of facilities by 10%.

An important part of preventive maintenance is condition based monitoring (CBM), which usually monitors rotating machines such as turbines, fans, pumps, motors, etc. Using CBM, the running status information can be recorded in real time. However, failure or wear predictions are not provided. These can only be provided through preventive maintenance, which brings a turning point: with more intelligent sensors, more powerful communication networks and computing platforms, people can create models, detect changes, and calculate the service life in detail.

In order to build an effective model, vibration, temperature, current and magnetic field need to be analyzed. Today’s wired and wireless communication methods support facility monitoring throughout the plant or company. Cloud based system brings us more analysis possibilities, so that operators and maintenance technicians can obtain data about machine status information in a simple way. However, the machine must have local intelligent sensors and communication infrastructure, which is the premise of obtaining additional analysis capability. What are these sensors like, what requirements need to be met, and what are the key features – these and other issues will be discussed in this article.

Machine life cycle display

With regard to condition monitoring, the following basic questions may need to be considered: how long can the equipment run before the necessary maintenance is carried out?

In general, logically speaking, the shorter the interval between finding a problem and starting maintenance, the better. However, in order to optimize the operation and maintenance costs or give full play to the maximum efficiency of the facility, professionals familiar with the characteristics of the machine need to rely on their knowledge and experience. These professionals are mainly from the bearing / lubrication field and have little experience in motor analysis, which is the weakest link. Professionals will eventually decide whether to repair or even replace it according to the actual life cycle (as shown in Figure 1) and the deviation of the actual state from the normal state.


Figure 1 The life cycle of the machine.

Unused machines are initially under a so-called warranty. This is an early stage of the life cycle. It is not ruled out that there will be faults in this stage, but this probability is relatively small and is generally related to production faults. Only in the next periodic maintenance stage, the maintenance personnel who have received corresponding training will start to carry out targeted intervention. Regardless of the actual state of the machine, they will perform routine maintenance on the machine at the specified time or after reaching the specified service time, such as changing the oil for the machine. In this case, the probability of failure during the maintenance interval is still very low. As the use time of the machine increases, it will gradually reach the condition monitoring stage. After that, prepare for failure. Figure 1 shows the following six changes, starting with the change of ultrasonic range (1), followed by vibration change (2). By analyzing the lubricating oil (3) or slightly increasing the temperature (4), the early signs of failure can be detected by perceptible noise (5) or heating (6) before the actual failure. Vibration is usually used to confirm aging. Figure 2 shows the vibration modes of three identical devices during their life cycle. All three machines are in the normal range in the initial stage. However, from the middle stage, the vibration increases more or less rapidly according to the specific load situation; In the later stage, it will increase exponentially and reach the critical range. Once the equipment reaches the critical range, immediate action is required.

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Figure 2 The vibration parameters change with time.

Condition monitoring through vibration analysis

The parameters such as output speed, gear ratio and the number of bearing components are closely related to the vibration mode analysis of the machine. Generally speaking, the vibration caused by the gearbox is expressed as a multiple of the shaft speed in the frequency domain, while the characteristic frequency of the bearing usually does not represent the harmonic component. In addition, vibrations caused by turbulence and cavitation are usually detected. They are usually related to the air and / or liquid flow in fans and pumps, so they are generally regarded as random vibration. They are usually static, and there is no difference in statistical characteristics. However, random vibration also has cyclostationarity, so it also has statistical characteristics. They are produced by the machine and change periodically, which is similar to the situation that each cylinder of internal combustion engine is ignited once every cycle.

Sensor orientation is also crucial. If a uniaxial sensor is used to measure the main linear vibration, the sensor must be adjusted according to the vibration direction. Multi axis sensor can also be used to record vibration in all directions, but based on its physical characteristics, single axis sensor has lower noise, wider measurement range and larger bandwidth.

Demand for vibration sensors

In order to widely use vibration sensors for condition monitoring, two important factors must be considered: low cost and small size. In the past, people usually used piezoelectric sensors, but now more and more accelerometers based on MEMS are used. They have higher resolution, excellent drift characteristics and sensitivity, as well as higher signal-to-noise ratio. In addition, they can detect very low frequency vibration almost close to the DC range. At the same time, it is also very energy-saving, so it is very suitable for battery powered wireless monitoring system. Compared with piezoelectric sensors, there is another advantage: the whole system can be integrated into a single shell (system level package). These so-called SIP solutions continuously integrate the following other important functions and jointly build intelligent systems: analog-to-digital converter, microcontroller with embedded firmware (special preprocessing), communication protocol and general interface, as well as various protection functions.

The integrated protection function is very important because the sensor element will be damaged due to excessive force. The integrated over range detection function can warn or disable the sensor components in the gyroscope by turning off the internal clock, so as to protect the sensor elements from damage. The SIP solution is shown in Figure 3.

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Figure 3 MEMS based system level package (left).

With the increasing demand in CBM field, the demand for sensors increases accordingly. For effective CBM, the requirement for sensor measurement range (full scale, i.e. FSR) is generally ± 50 G.

Since the acceleration is proportional to the square of the frequency, these high acceleration forces can be achieved relatively quickly. Formula 1 can prove this:


The variable represents acceleration, f represents frequency and D represents vibration amplitude. Therefore, for example, when the vibration is 1 kHz, an amplitude of 1 µ m will produce an acceleration of 39.5 G.

As for noise performance, this value should be very low in the widest possible frequency range (from close to DC to the middle range of tens of kHz), so that bearing noise can be detected at very low speeds, among other factors. However, it can also be seen that vibration sensor manufacturers are facing a major challenge, especially for multi axis sensors. Only a few manufacturers can provide multi axis sensors with bandwidth greater than 2 kHz and sufficiently low noise. ADI company (ADI) has developed adxl356 / adxl357 three-axis sensor series suitable for CBM applications. This series of products have excellent noise performance and temperature stability. In addition to the limited bandwidth of 1.5 kHz (resonant frequency = 5.5 kHz), these accelerometers can still provide important condition monitoring readings for low-speed equipment such as wind turbines.

The single axis sensor in the adxl100x series is suitable for higher bandwidth. They provide a bandwidth of up to 24 kHz (resonant frequency = 45 kHz) and a g range of up to ± 100g at very low noise levels. Due to its high bandwidth, the sensor series can detect most fault problems in rotating machinery (sliding bearing damage, imbalance, friction, looseness, tooth defect, bearing wear and cavitation).

Analysis methods that can be adopted for condition based monitoring

Machine state analysis in CBM can be completed by many methods. The most common methods are time domain analysis, frequency domain analysis, and both.

1. Time based analysis

In the time domain vibration analysis, the effective value (root mean square, i.e. RMS), peak to peak value and vibration amplitude will be considered (see Figure 4).

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Figure 4 Amplitude, effective value and peak value of harmonic vibration signal.

The peak to peak value reflects the maximum deflection of the motor shaft, so the maximum load can be obtained. The amplitude value represents the amplitude of vibration and identifies abnormal vibration phenomena. However, the duration of vibration or the energy during vibration and the destructive force of vibration will not be considered. Therefore, the effective value is generally the most meaningful value, because it considers not only the vibration duration, but also the vibration amplitude. By analyzing the dependence of all these parameters on the motor speed, the correlation of the statistical threshold of RMS vibration can be obtained.

It turns out that this kind of analysis is very simple because it requires neither basic system knowledge nor any type of spectral analysis.

2. Frequency based analysis

Using frequency based analysis, the time-varying vibration signal can be decomposed into frequency components by fast Fourier transform (FFT). The resulting spectrum diagram of amplitude and frequency relationship helps to monitor specific frequency components and their harmonics and sidebands (see Figure 5).

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Figure 5 Spectrum diagram of the relationship between vibration and frequency.

FFT is a widely used method in vibration analysis, especially for detecting bearing damage. With this method, the corresponding components can be assigned to each frequency component. Through FFT, the main frequency of repeated pulse generated when some faults are caused by the contact between rolling parts and defect area can be filtered out. Because of their different frequency components, different types of bearing damage (outer ring, inner ring or ball bearing damage) can be distinguished. However, this requires accurate information about bearings, motors and the entire system.

In addition, the FFT process needs to provide discrete-time blocks that repeatedly record and process vibration in the microcontroller. Although this analysis requires more computational power than time-domain analysis, it can carry out more detailed damage analysis.

3. Combination of time domain and frequency domain analysis

This kind of analysis is the most comprehensive because it combines the advantages of both methods. The statistical analysis in the time domain provides information about the variation of the vibration intensity of the system with time and whether they are within the allowable range. Frequency domain analysis can monitor the speed in the form of basic frequency, and can also monitor the harmonic component required to accurately identify the fault characteristics.

The tracking of the fundamental frequency is particularly decisive because the RMS and other statistical parameters vary with speed. If the statistical parameters change significantly compared with the last measurement, the basic frequency must be checked to avoid false positives.

For these three analysis methods, the measured values will change with time. The monitoring system may first need to record the operation or generate so-called fingerprints. Then compare with the continuously recorded data. When the deviation is too large or exceeds the corresponding threshold, a response is required. As shown in Fig. 6, the possible response may be a warning (2) or an alarm (4). Depending on the severity, maintenance personnel may be required to correct these deviations immediately.


Figure 6 Threshold and response to FFT.

Implement CBM through magnetic field analysis

Due to the rapid development of integrated magnetometer, measuring the stray magnetic field around the motor is another promising method for condition monitoring of rotating machines. The measurement is non-contact; In other words, there is no direct connection between the machine and the sensor. Like vibration sensors, magnetic field sensors are available in both uniaxial and multiaxial versions.

For fault detection, the stray magnetic field shall be measured axially (parallel to the motor shaft) and radially (at right angles to the motor shaft). The radial magnetic field is usually weakened by the stator core and motor housing. At the same time, it will be significantly affected by the air gap magnetic flux. The axial magnetic field is generated by the current of the squirrel cage rotor and the end winding of the stator. The position and direction of the magnetometer play a decisive role in measuring the two magnetic fields. Therefore, it is recommended to choose a suitable position close to the shaft or motor housing. It is absolutely necessary to measure the magnetic field intensity, because it is directly related to the temperature. Therefore, in most cases, today’s magnetic field sensors include integrated temperature sensors. In addition, the sensor shall be calibrated and the temperature drift compensation correction shall be implemented.

FFT is used to monitor the state of motor based on magnetic field, just like vibration measurement. However, for motor condition evaluation, even low frequencies in the range of a few Hz to about 120 Hz are sufficient. The line frequency is very prominent, and the frequency spectrum of low-frequency component is mainly in case of fault.

When the rotating rod of squirrel cage rotor is broken, the sliding value also plays a decisive role. It is load dependent, ideally 0% without load. When the rated load is adopted, the value of the machine in normal operation is between 1% and 5%, which will increase accordingly in case of failure. For CBM, it should be measured under the same load conditions to eliminate the impact of different loads.

Status of preventive maintenance

No matter what type of condition monitoring is used, even if the most intelligent monitoring scheme is adopted, it cannot be 100% guaranteed that there will be no unexpected shutdown, failure or safety risk. These risks can only be reduced. However, preventive maintenance has attracted more and more attention and is becoming an important topic in the industry. It is considered to be a clear prerequisite for the sustainable success of production facilities in the future. However, to achieve this, we need to adopt unique technologies, and we must continue to innovate and accelerate development. The profit and loss deficit is reflected in the comparison of customers’ interests and costs.

Nevertheless, many industrial enterprises have recognized the importance of preventive maintenance, which is an important factor in determining success, and therefore an opportunity to carry out future business – this opportunity is not limited to the field of maintenance services. Although facing great challenges, especially in the field of data analysis, preventive maintenance has high technical feasibility. However, at present, preventive maintenance has strong opportunistic characteristics. It is expected that the future business model will mainly depend on software components, and the value-added share brought by hardware will continue to decline. In short, because the machine runs for a long time and generates high value, the current investment in preventive maintenance hardware and software has been worth it.


1 “ABB Ability Smart Sensor jetzt für den europäischen Markt verfügbar”. Abb, April 2017.

Introduction to the author

Thomas brand joined ADI in Munich, Germany in October 2015, when he was still studying for a master’s degree. From May 2016 to January 2017, he participated in the field application engineer trainee program of ADI company. Later, in February 2017, he began to hold the position of field application engineer, mainly responsible for major industrial customers. In addition, he also focused on Industrial Ethernet and provided support for relevant topics in Central Europe.

He graduated from the Joint Education University in Mosbach, Germany, majoring in electrical engineering, and then received a master’s degree in international sales from the University of Applied Sciences in Konstanz, Germany.


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