In this paper, a fourth-order Kalman filter (KF) algorithm is implemented in the wireless sensor node to estimate the temperatures of the stator winding, the rotor cage and the stator core in the induction machine. Three separate wireless sensor nodes are used as the data acquisition systems for different input signals. Six Hall sensors are used to acquire the three-phase stator currents and voltages of the induction machine. All of them are processed to root mean square (rms) in ampere and volt. A rotary encoder is mounted for the rotor speed and Pt-1000 is used for the temperature of the coolant air. The processed signals in the physical unit are transmitted wirelessly to the host wireless sensor node, where the KF is implemented with fixed-point arithmetic in Contiki OS. Time-division multiple access (TDMA) is used to make the wireless transmission more stable. Compared to the floating-point implementation, the fixed-point implementation has the same estimation accuracy at only about one-fifth of the computation time. The temperature estimation system can work under any work condition as long as there are currents through the machine. It can also be rebooted for estimation even when wireless transmission has collapsed or packages are missing.
Electrical machines are widely used in the
industry, especially with the increasing interest in electric and hybrid
electric vehicles. The thermal behavior of an induction machine largely
determines the maximum lifetime, to cope with overload conditions and also
the accuracy in a high-performance controller
Meanwhile the WSNs have many applications, such as industry, environment
monitoring, tracking of things and internet of things. A number of methods
for temperature monitoring of induction machines can be found in the
literature. Some of the methods do not provide satisfying results or can only
estimate the temperatures of stator winding and rotor cages without a stator
core
In conclusion, many of the monitoring applications for the electrical machine
based on a WSN can be found in the literature. However, none of them has
implemented a temperature estimation algorithm on a resource-limited wireless
sensor node. The temperature monitoring system of an induction machine based
on a WSN is explored in this paper. We focus on the algorithmic
implementation on the wireless sensor network. The input signal is the
processing of the signals of a single node distributed over different nodes
and transmitted to the host node, where the algorithm is implemented.
Section 2 gives a description of the system. The implementation of a wireless
transducer interface module (WTIM) and a network capable application
processor (NCAP,
The thermal network of the machine can be summarized as the following
Fig.
The thermal network of the machine.
From the above figure, the state-space equations of the system are defined as
the following equations:
In the simplified thermal model,
The temperatures of the stator winding and rotor cage will increase largely.
Normally it will be much higher than the reference ambient temperature. The
rising temperature makes the resistance greater by more than 40 %. The
electrical resistances will increase as the machine is running. So the
ignored increasing temperature should be considered to calculate resistance,
which is with respect to time. All in all, the stator winding loss can be
calculated much more accurately than that of the constant value of the
electrical resistance.
The state-space equations of the system can be acquired by calculating the
losses
In the state equations,
The platform is the Preon32
wireless sensor node produced by Virtenio GmbH. It contains a 32-bit ARM
Cortex-M3 micro-controller with 256 kB flash memory for programming and
64 kB RAM memory for data. A 2.4 GHz wireless transceiver which is
compliant with the IEEE 802.15.4 standard can for example be used for ZigBee
or 6LoWPAN communication. Two 12-bit analog-to-digital converters (ADCs) with
a maximum sampling rate of 1 M samples/s are provided by the platform
The whole software package is comprised as follows: Contiki, ARM CMSIS
Library, Preon32 platform, Preon32 firmware and the MDT Smart Transducer
Library (MSTL). Figure
The architecture of the WSN software.
Structure of the wireless sensor system.
Based on the proposed KF algorithm, four types of signals are acquired as the
inputs of the algorithm. Three Preon32 nodes are implemented as the WTIMs to
acquire coolant air temperature, rotor speed, effective current and voltage.
Data acquisition, data preprocessing and data transmission are performed by
these WTIMs. Another node is implemented as the NCAP to receive the data from
different WTIMs and to process the KF algorithm for temperature estimation.
The structure of the temperature estimation system on WSN is shown in
Fig.
Preon32 provides multiple I/O interfaces for connection to external peripheral digital I/O pins which could be used for the acquisition of rotor speed. Analog signals such as the coolant air temperature, the three-phase currents and voltages can be captured with the integrated ADC with a resolution of 12 bits and a possible sampling rate of up to 1 million samples per second. The conditioning boards were designed for connecting the sensors with Preon32 sensor nodes and conditioning the analog signal.
Six sensors based on the Hall effect for the
three-phase currents and voltages are first mounted on a data acquisition
board in the paper by
Conditioning board of currents and voltages without housing.
Conditioning board currents and voltages with housing.
The coolant air temperature is one of the inputs which should be measured and
transmitted wirelessly by a Preon32 sensor node. Pt-1000 and a commercial
conditioning board are used for the temperature acquisition. The output
voltage of the conditioning board provided together with the sensor can be
calibrated to the range of
In order to acquire the speed of the rotor, a rotary encoder “ROD 426
B-6000” from HEIDENHAIN GmbH is used. A conditioning circuit board shown in
Fig.
Conditioning board for the rotary encoder.
Hardware of the rotor speed acquisition.
The data acquisition system (DAQ) is implemented in WTIMs based on the MSTL
which provides a universal interface to a variety of transducers. The
implementation also follows the IEEE1451 family of standards in many places.
The
Hall sensors are mounted on the conditioning board with low-pass filters to
process analog three-phase currents and voltages
The measurement chain of the effective current and voltage is taken as an
example to illustrate the measurement process, which is shown in
Fig.
Measurement chain of the effective current and voltage.
As data are acquired, filtered and transmitted continuously, the calculation
time for each step must be considered. Buffers for data storage are allocated
using MEMB memory block allocators, which is described in the documents
Detailed processing time division of analog signals.
The total acquisition and conversion time for one block (sampling time
A rotary encoder (ROD 426B-6000) is mounted to the end of the machine shaft
and connected to a conditioning board. A WTIM node is used to transfer the
number of the pulse into the real rotor speed using
The acquisition of the generated pulses.
The rotation speed can be defined in Eq. (
The general structure of the implemented WTIM is shown in Fig.
The structure of the implemented WTIMs.
This section discusses the implementation of the KF algorithm based on the IEEE1451 standard in the NCAP. The minimum implementation of the IEEE1451 standard has been integrated into both the WTIM and the NCAP. Sensors and actuators which are connected to the WTIM can be managed by wireless commands from the NCAP.
The Kalman filter is a set of mathematical equations that provides an
efficient computational (recursive) means to estimate the state of a process,
in a way that minimizes the mean of the squared
error
The integration of the KF into the Contiki system stack of the NCAP.
The equations of the prediction stage shown in
Eqs. (
The model above is a continuous time system which cannot be processed by
computer. Euler's approximation is used to
discretize the model, so that the sampled data
can be used in the KF algorithm. According to the definition of the
derivative, Eq. (
The workflow of the KF algorithm process.
The data range and the resolution of the variables.
The equations of the correction stage are responsible for the feedback –
i.e., for incorporating a new measurement into a priori estimation to obtain
an improved a posteriori estimation
In our application, the KF algorithm is integrated into the NCAP to estimate
the temperatures of stator windings, the rotor cage and the stator core of an
induction machine. The Preon32 sensor node is resource restricted with
respect to low costs, low power consumption and small memory size. In order
to be implemented in the NCAP, the algorithm should be simple and efficient.
The integration of the KF layer into the Contiki system stack is shown in
Fig.
The structure of the implemented NCAP.
6LoWPAN is defined encapsulation and header compression mechanisms that allow
IPv6 packets to be sent to and received from IEEE802.15.4 links, whose full
name is IPv6 over Low power Wireless Personal Area Networks
The usage of RAM on the NCAP (total memory: 64 kB).
The usage of flash memory on the NCAP (total memory: 256 kB).
The KF algorithm is first implemented in MATLAB. It proved both in simulation
and offline experiments on the test bench that the temperatures can be
accurately estimated. In order to be implemented on the resource-restricted
sensor node, the same KF algorithm is implemented in the C programming
language using floating-point arithmetic on the Eclipse IDE platform. The
workflow of the
Compared to the implementation in MATLAB and Eclipse in the C language, implementation on the Preon32 sensor node using Contiki OS faces several challenges.
Firstly, the methods to allocate and free memory space are different between
the standard C library and Contiki OS. The standard C library allocates heap
memory using the
The second challenge is that the Preon32 does not have a floating-point unit.
It is clear that the floating-point implementation cannot run online. As a
result, fixed-point arithmetic is used for the implementation. In order to
transfer the existing KF algorithm from floating-point to fixed-point
representation, the proper Q format (Qm.n) defined in the document
Comparison of TDMA and CSMA.
The sequence on the NCAP side.
The sequence on the WTIM side.
Thus the Q1.31 format is used for the arithmetic with a resolution of
The third challenge is the estimation time for every step. The ARM Cortex-M3
processor provides the CMSIS DSP library, which contains matrix functions in
fixed point
Contiki OS is an event-driven system which is managed by protothreads. In
order to operate different WTIMs, to manage the message transmission and to
process the KF algorithm, several functional processes are implemented in the
NCAP. The structure of the implemented processes is shown in
Fig.
Error and NRMSE of the estimated temperatures under S1.
Error and NRMSE of the estimated temperatures under S6.
The
In the implementation of the KF algorithm in the NCAP, all the memory blocks
are allocated statically so that fragmentation can be avoided
Comparison of measured and estimated temperatures under S1.
Comparison of measured and estimated temperatures under S6.
The usage of the flash memory for programming on the NCAP is shown in
Fig.
The system gets the data from different buffers to generate the input, which
costs 120
In the WSN system, the IEEE1451.5 standard defines the communication interfaces between the NCAP and WTIMs. The 6LoWPAN communication protocol is implemented in the network layer and UDP is used at the transport layer to comply with this specification in the standard.
Three WTIMs continuously transmit data streaming to the NCAP. Radio channel
collision, which is caused by two of the nodes sending data at the same time,
is a great concern in the implementation. Carrier-sense multiple access
(CSMA) and time-division multiple access (TDMA) are implemented in the MAC
layer as the channel access methods, which can be selected according to
different requirements of the application. The mechanism and the
implementation of these two methods are out of the scope of this paper. Both
CSMA and TDMA can be applied in this system through the experiment.
Table
CSMA prevents collisions by repeatedly detecting the channel and waiting for it to become available. So when a large number of nodes are operated in WSN with CSMA, the channel utilization is normally lower than with TDMA. When data from three different WTIMs are transmitted to the NCAP, collisions would happen or packages would be lost. Either of these events can influence the estimation results or block the process of the algorithm.
TDMA is an alternative mechanism to coordinate each node which is divided into time frames and each time frame is further divided into a fixed number of time slots. By using TDMA, data transmissions operate in a completely predictable way, which can largely reduce the collisions and almost prevent the packages from missing. Fewer collisions and more stable transmission have higher priority when determining channel access method. As a result, TDMA is used for this WSN system.
The sequence on the NCAP side is shown in Fig.
The sequence on the WTIM side is shown in Fig.
The structure of the test bench is shown in
Fig.
The other experiment under intermittent-load S6 (6 min no load followed by
4 min full load) is also performed on the test bench. The estimated and
measured temperatures are shown in Fig.
This paper describes the implementation of the temperature estimation system of induction machines on a WSN. The fourth-order KF with fixed-point arithmetic is implemented in the NCAP. Three WTIMs are implemented as the data acquisition systems. Compared to the floating-point implementation, the fixed point had the same estimation accuracy at only about one-fifth of the computation time. The KF algorithm is independent of the control strategy and the running conditions. That means no matter what the rotor speed is, and what the mechanical load is, as long as there are currents through the stator winding, the temperature can be estimated correctly. The experiments prove that the KF implementation is suitable for real-time temperature estimation on a resource-limited wireless sensor node. If wireless transmission has collapsed or packages are missing, the system can be rebooted for temperature estimation.
The underlying research data are stored in an internal system. All measurement data are not publicly available and can be accessed from the authors upon request.
The structure of the test bench.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Sensor/IRS2 2017”. It is a result of the AMA Conferences, Nuremberg, Germany, 30 May–1 June 2017.
I would like to express my appreciation to my doctoral thesis advisor, Clemens Gühmann, who kept giving me invaluable guidance for the research. I would like to thank my colleague, Jürgen Funck, and my student Wenjun Zhu for their help. Edited by: Andreas König Reviewed by: three anonymous referees